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Biblioteca

MARC Lista (tabellare)
Automated software engineering
Automated software engineering
Pubbl/distr/stampa [Dordrecht], : Kluwer Academic Publishers
Disciplina 005.1
Soggetto topico Software engineering
Expert systems (Computer science)
Génie logiciel
Systèmes experts (Informatique)
Soggetto genere / forma Periodicals.
ISSN 1573-7535
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Record Nr. UNISA-996216672303316
[Dordrecht], : Kluwer Academic Publishers
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Bioinspired Intelligence (IWOBI), 2015 4th International Work Conference on / / Carlos M. Travieso-González, Jesús B. Alonso-Hernández, Karmele López de Ipiña, editors
Bioinspired Intelligence (IWOBI), 2015 4th International Work Conference on / / Carlos M. Travieso-González, Jesús B. Alonso-Hernández, Karmele López de Ipiña, editors
Pubbl/distr/stampa [Place of publication not identified] : , : IEEE, , 2015
Descrizione fisica 1 online resource
Disciplina 006.33
Soggetto topico Medical informatics
Expert systems (Computer science)
ISBN 1-4673-7846-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti 2015 4th International Work Conference on Bioinspired Intelligence
2015 4th International Work Conference on Bioinspired Intelligence (IWOBI)
Bioinspired Intelligence
Record Nr. UNINA-9910131506103321
[Place of publication not identified] : , : IEEE, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Bioinspired Intelligence (IWOBI), 2015 4th International Work Conference on / / Carlos M. Travieso-González, Jesús B. Alonso-Hernández, Karmele López de Ipiña, editors
Bioinspired Intelligence (IWOBI), 2015 4th International Work Conference on / / Carlos M. Travieso-González, Jesús B. Alonso-Hernández, Karmele López de Ipiña, editors
Pubbl/distr/stampa [Place of publication not identified] : , : IEEE, , 2015
Descrizione fisica 1 online resource
Disciplina 006.33
Soggetto topico Medical informatics
Expert systems (Computer science)
ISBN 1-4673-7846-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti 2015 4th International Work Conference on Bioinspired Intelligence
2015 4th International Work Conference on Bioinspired Intelligence (IWOBI)
Bioinspired Intelligence
Record Nr. UNISA-996278331103316
[Place of publication not identified] : , : IEEE, , 2015
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
BuildSys '16 : proceedings of the 3rd ACM Conference on Systems for Energy-Efficient Built Environments : Stanford, California, USA, November 15-17, 2016 / / sponsored by ACM
BuildSys '16 : proceedings of the 3rd ACM Conference on Systems for Energy-Efficient Built Environments : Stanford, California, USA, November 15-17, 2016 / / sponsored by ACM
Pubbl/distr/stampa New York : , : ACM, , 2016
Descrizione fisica 1 online resource (273 pages)
Disciplina 629.89
Soggetto topico Intelligent buildings
Buildings - Energy conservation - Data processing
Expert systems (Computer science)
Soggetto genere / forma Electronic books.
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti BuildSys 2016 : proceedings of the 3rd Association for Computing Machinery Conference on Systems for Energy-Efficient Built Environments : Stanford, California, USA, November 15-17, 2016
Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments
Proceedings of the 3rd Association for Computing Machinery International Conference on Systems for Energy-Efficient Built Environments
Record Nr. UNINA-9910376443903321
New York : , : ACM, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Business intelligence and analytics : systems for decision support / / Ramesh Sharda, Dursun Delen, Efraim Turban ; with contributions by J. E. Aronson, Ting-Peng Liang, David King
Business intelligence and analytics : systems for decision support / / Ramesh Sharda, Dursun Delen, Efraim Turban ; with contributions by J. E. Aronson, Ting-Peng Liang, David King
Autore Sharda Ramesh
Edizione [Tenth edition.]
Pubbl/distr/stampa Boston, [Massachusetts] : , : Pearson, , 2014
Descrizione fisica 1 online resource (689 pages) : illustrations, tables
Disciplina 658.403
Collana Always Learning
Soggetto topico Decision support systems
Expert systems (Computer science)
Business intelligence
ISBN 1-292-00926-8
9781292009209
9781292009261
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Contents -- Preface -- About the Authors -- Part I Decision Making and Analytics: An Overview -- Chapter 1 An Overview of Business Intelligence, Analytics, and Decision Support -- 1.1 Opening Vignette: Magpie Sensing Employs Analytics to Manage a Vaccine Supply Chain Effectively and Safely -- 1.2 Changing Business Environments and Computerized Decision Support -- The Business Pressures-Responses-Support Model -- 1.3 Managerial Decision Making -- The Nature of Managers' Work -- The Decision-Making Process -- 1.4 Information Systems Support for Decision Making -- 1.5 An Early Framework for Computerized Decision Support -- The Gorry and Scott-Morton Classical Framework -- Computer Support for Structured Decisions -- Computer Support for Unstructured Decisions -- Computer Support for Semistructured Problems -- 1.6 The Concept of Decision Support Systems (DSS) -- DSS as an Umbrella Term -- Evolution of DSS into Business Intelligence -- 1.7 A Framework for Business Intelligence (BI) -- Definitions of BI -- A Brief History of BI -- The Architecture of BI -- Styles of BI -- The Origins and Drivers of BI -- A Multimedia Exercise in Business Intelligence -- Application Case 1.1 Sabre Helps Its Clients Through Dashboards and Analytics -- The DSS-BI Connection -- 1.8 Business Analytics Overview -- Descriptive Analytics -- Application Case 1.2 Eliminating Inefficiencies at Seattle Children's Hospital -- Application Case 1.3 Analysis at the Speed of Thought -- Predictive Analytics -- Application Case 1.4 Moneyball: Analytics in Sports and Movies -- Application Case 1.5 Analyzing Athletic Injuries -- Prescriptive Analytics -- Application Case 1.6 Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Network -- Analytics Applied to Different Domains -- Analytics or Data Science?.
1.9 Brief Introduction to Big Data Analytics -- What Is Big Data? -- Application Case 1.7 Gilt Groupe's Flash Sales Streamlined by Big Data Analytics -- 1.10 Plan of the Book -- Part I: Business Analytics: An Overview -- Part II: Descriptive Analytics -- Part III: Predictive Analytics -- Part IV: Prescriptive Analytics -- Part V: Big Data and Future Directions for Business Analytics -- 1.11 Resources, Links, and the Teradata University Network Connection -- Resources and Links -- Vendors, Products, and Demos -- Periodicals -- The Teradata University Network Connection -- The Book's Web Site -- Chapter Highlights -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case Nationwide Insurance Used BI to Enhance Customer Service -- References -- Chapter 2 Foundations and Technologies for Decision Making -- 2.1 Opening Vignette: Decision Modeling at HP Using Spreadsheets -- 2.2 Decision Making: Introduction and Definitions -- Characteristics of Decision Making -- A Working Definition of Decision Making -- Decision-Making Disciplines -- Decision Style and Decision Makers -- 2.3 Phases of the Decision-Making Process -- 2.4 Decision Making: The Intelligence Phase -- Problem (or Opportunity) Identification -- Application Case 2.1 Making Elevators Go Faster! -- Problem Classification -- Problem Decomposition -- Problem Ownership -- 2.5 Decision Making: The Design Phase -- Models -- Mathematical (Quantitative) Models -- The Benefits of Models -- Selection of a Principle of Choice -- Normative Models -- Suboptimization -- Descriptive Models -- Good Enough, or Satisficing -- Developing (Generating) Alternatives -- Measuring Outcomes -- Risk -- Scenarios -- Possible Scenarios -- Errors in Decision Making -- 2.6 Decision Making: The Choice Phase -- 2.7 Decision Making: The Implementation Phase -- 2.8 How Decisions Are Supported.
Support for the Intelligence Phase -- Support for the Design Phase -- Support for the Choice Phase -- Support for the Implementation Phase -- 2.9 Decision Support Systems: Capabilities -- A DSS Application -- 2.10 DSS Classifications -- The AIS SIGDSS Classification for DSS -- Other DSS Categories -- Custom-Made Systems Versus Ready-Made Systems -- 2.11 Components of Decision Support Systems -- The Data Management Subsystem -- The Model Management Subsystem -- Application Case 2.2 Station Casinos Wins by Building Customer Relationships Using Its Data -- Application Case 2.3 SNAP DSS Helps OneNet MakeTelecommunications Rate Decisions -- The User Interface Subsystem -- The Knowledge-Based Management Subsystem -- Application Case 2.4 From a Game Winner to a Doctor! -- Chapter Highlights -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case Logistics Optimization in a Major Shipping Company (CSAV) -- References -- Part II Descriptive Analytics -- Chapter 3 Data Warehousing -- 3.1 Opening Vignette: Isle of Capri Casinos Is Winning with Enterprise Data Warehouse -- 3.2 Data Warehousing Definitions and Concepts -- What Is a Data Warehouse? -- A Historical Perspective to Data Warehousing -- Characteristics of Data Warehousing -- Data Marts -- Operational Data Stores -- Enterprise Data Warehouses (EDW) -- Metadata -- Application Case 3.1 A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry -- 3.3 Data Warehousing Process Overview -- Application Case 3.2 Data Warehousing Helps MultiCare Save More Lives -- 3.4 Data Warehousing Architectures -- Alternative Data Warehousing Architectures -- Which Architecture Is the Best? -- 3.5 Data Integration and the Extraction, Transformation, and Load (ETL) Processes -- Data Integration.
Application Case 3.3 BP Lubricants Achieves BIGS Success -- Extraction, Transformation, and Load -- 3.6 Data Warehouse Development -- Application Case 3.4 Things Go Better with Coke's Data Warehouse -- Data Warehouse Development Approaches -- Application Case 3.5 Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing -- Additional Data Warehouse Development Considerations -- Representation of Data in Data Warehouse -- Analysis of Data in the Data Warehouse -- OLAP Versus OLTP -- OLAP Operations -- 3.7 Data Warehousing Implementation Issues -- Application Case 3.6 EDW Helps Connect State Agencies in Michigan -- Massive Data Warehouses and Scalability -- 3.8 Real-Time Data Warehousing -- Application Case 3.7 Egg Plc Fries the Competition in Near Real Time -- 3.9 Data Warehouse Administration, Security Issues, and Future Trends -- The Future of Data Warehousing -- 3.10 Resources, Links, and the Teradata University Network Connection -- Resources and Links -- Cases -- Vendors, Products, and Demos -- Periodicals -- Additional References -- The Teradata University Network (TUN) Connection -- Chapter Highlights -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case Continental Airlines Flies High with Its Real-Time Data Warehouse -- References -- Chapter 4 Business Reporting, Visual Analytics, and Business Performance Management -- 4.1 Opening Vignette:Self-Service Reporting Environment Saves Millions for Corporate Customers -- 4.2 Business Reporting Definitions and Concepts -- What Is a Business Report? -- Application Case 4.1 Delta Lloyd Group Ensures Accuracy and Efficiency in Financial Reporting -- Components of the Business Reporting System -- Application Case 4.2 Flood of Paper Ends at FEMA -- 4.3 Data and Information Visualization.
Application Case 4.3 Tableau Saves Blastrac Thousands of Dollars with Simplified Information Sharing -- A Brief History of Data Visualization -- Application Case 4.4 TIBCO Spotfire Provides Dana-Farber Cancer Institute with Unprecedented Insight into Cancer Vaccine Clinical Trials -- 4.4 Different Types of Charts and Graphs -- Basic Charts and Graphs -- Specialized Charts and Graphs -- 4.5 The Emergence of Data Visualization and Visual Analytics -- Visual Analytics -- High-Powered Visual Analytics Environments -- 4.6 Performance Dashboards -- Application Case 4.5 Dallas Cowboys Score Big with Tableau and Teknion -- Dashboard Design -- Application Case 4.6 Saudi Telecom Company Excels with Information Visualization -- What to Look For in a Dashboard -- Best Practices in Dashboard Design -- Benchmark Key Performance Indicators with Industry Standards -- Wrap the Dashboard Metrics with Contextual Metadata -- Validate the Dashboard Design by a Usability Specialist -- Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard -- Enrich Dashboard with Business Users' Comments -- Present Information in Three Different Levels -- Pick the Right Visual Construct Using Dashboard Design Principles -- Provide for Guided Analytics -- 4.7 Business Performance Management -- Closed-Loop BPM Cycle -- Application Case 4.7 IBM Cognos Express Helps Mace for Faster -- 4.8 Performance Measurement -- Key Performance Indicator (KPI) -- Performance Measurement System -- 4.9 Balanced Scorecards -- The Four Perspectives -- The Meaning of Balance in BSC -- Dashboards Versus Scorecards -- 4.10 Six Sigma as a Performance Measurement System -- The DMAIC Performance Model -- Balanced Scorecard Versus Six Sigma -- Effective Performance Measurement -- Application Case 4.8 Expedia.com's Customer Satisfaction Scorecard -- Chapter Highlights -- Key Terms -- Questions for Discussion.
Record Nr. UNINA-9910151592903321
Sharda Ramesh  
Boston, [Massachusetts] : , : Pearson, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Case-based reasoning research and development : 29th International Conference, ICCBR 2021, Salamanca, Spain, September 13-16, 2021 : proceedings / / Antonio A. Sánchez-Ruiz, Michael W. Floyd
Case-based reasoning research and development : 29th International Conference, ICCBR 2021, Salamanca, Spain, September 13-16, 2021 : proceedings / / Antonio A. Sánchez-Ruiz, Michael W. Floyd
Autore Sánchez-Ruiz Antonio A.
Pubbl/distr/stampa Cham, Switzerland : , : Springer International Publishing, , [2021]
Descrizione fisica 1 online resource (337 pages)
Disciplina 006.33
Collana Lecture Notes in Computer Science
Soggetto topico Expert systems (Computer science)
Case-based reasoning
ISBN 3-030-86957-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- The Bites Eclectic: Critique-Based Conversational Recommendation for Diversity-Focused Meal Planning -- 1 Introduction -- 2 Background -- 2.1 Diversity -- 2.2 Critiquing -- 2.3 Recipe Recommendation and Diversity -- 3 DiversityBite: A Conversational Dynamic Critique-Based Recommender -- 3.1 Recipe Case Representation and Similarity -- 3.2 Diversity-Focused Conversational Critique -- 4 Evaluation for DiversityBite -- 4.1 Recipe Dataset -- 4.2 Evaluation Study -- 4.3 Evaluation Results -- 5 Discussion and Future Work -- References -- Evaluation of Similarity Measures for Flight Simulator Training Scenarios -- 1 Introduction -- 2 Related Work -- 3 Problem Definition: Pilot Competencies and Event Sets -- 4 Case Retrieval of Scenario Event Sets -- 5 Evaluation of Similarity Measures -- 5.1 Experimental Setup -- 5.2 Analysis -- 6 Results and Discussion -- 7 Conclusions -- References -- Instance-Based Counterfactual Explanations for Time Series Classification -- 1 Introduction -- 2 Related Work -- 3 Good Counterfactuals for Time Series: Key Properties -- 4 Native Guide: Counterfactual XAI for Time Series -- 5 Testing Native Guide: Two Comparative Experiments -- 5.1 Experiment 1: Probing Proximity and Sparsity -- 5.2 Experiment 2: Exploring Plausibility and Diversity -- 6 Conclusion and Future Directions -- References -- User Evaluation to Measure the Perception of Similarity Measures in Artworks -- 1 Introduction -- 2 Related Work About Similarity -- 3 Methodology for Learning Similarity Measures Reflecting Human Perceptions -- 3.1 Methodology for Learning Perception Aware Similarity Measures -- 3.2 Profile Definition -- 4 Experiment on the Perception of Similarity for Artworks -- 4.1 Definition of Local Similarity Measures -- 4.2 Data Gathering of Perceived Similarity -- 4.3 Experimental Results.
5 Conclusions and Future Work -- References -- Measuring Financial Time Series Similarity with a View to Identifying Profitable Stock Market Opportunities -- 1 Introduction -- 2 Related Work -- 3 From Prices to Cases -- 4 Similarity in Financial Time Series -- 4.1 The Problem with Correlation -- 4.2 An Adjusted Correlation Metric -- 4.3 A Novel Similarity Metric for Returns-Based Time-Series -- 4.4 Most and Least Similar Cases -- 5 Evaluation -- 5.1 Predicting Monthly Returns -- 5.2 Comparing Trading Strategies -- 6 Conclusion and Future Work -- References -- A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries -- 1 Introduction -- 2 Related Work -- 3 CBR for Injury Prediction and Explanation -- 3.1 Representing Training Load -- 3.2 Representing Injury Cases -- 3.3 Balancing the Case Base -- 3.4 Task 1: Predicting Training Breaks -- 3.5 Task 2: Explaining Training Breaks -- 4 Evaluation -- 4.1 Setup -- 4.2 Evaluating Prediction Accuracy -- 4.3 Evaluating Injury Explanations -- 5 Conclusions -- References -- Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists -- 1 Introduction -- 2 Related Work -- 3 Case and Problem Definition -- 4 BCBR Framework -- 4.1 Bayesian Inference -- 4.2 Case Base Creation and CBR Engine -- 4.3 Example: NBI Estimates, Case Retrieval and CBR Case -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experiment 1: Answer Classification Performance (Baselines) -- 5.3 Experiment 2: Trustworthiness of Constructed Checklists -- 5.4 Experiment 3: Evaluation of Constructed Checklists -- 6 Conclusion -- References -- Revisiting Fast and Slow Thinking in Case-Based Reasoning -- 1 Introduction -- 2 Background -- 3 Feature Selection -- 3.1 Feature Selection for Parsimonious Search -- 3.2 Modelling Desirability of Feature Sets -- 3.3 Brute Force Approach -- 3.4 Greedy Approach.
3.5 Experimental Analysis -- 4 Constraint-Based Switching -- 4.1 Model 2D -- 4.2 Experimental Analysis -- 5 Complexity Measure for Dichotomous Models -- 5.1 Footprint Size -- 5.2 Footprint with Time -- 5.3 Experimental Analysis -- 6 Conclusion and Future Work -- References -- Harmonizing Case Retrieval and Adaptation with Alternating Optimization -- 1 Introduction -- 2 Background -- 3 Alternating Optimization of Retrieval and Adaptation -- 4 Testbed System Design -- 4.1 Loss Function -- 4.2 Testbed Retrieval and Adaptation -- 4.3 Testbed Training and Testing Procedures -- 5 Evaluation -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 6 Guidelines for Applying AO to Train CBR Components -- 7 Future Work -- 8 Conclusion -- References -- Adaptation Knowledge Discovery Using Positive and Negative Cases -- 1 Introduction -- 2 Motivations and Preliminaries -- 2.1 Assumptions and Notations About CBR -- 2.2 Boolean Setting Illustrated with a Boolean Function Example -- 2.3 Itemset Extraction -- 3 Exploiting Case Variations for Adaptation Knowledge Discovery with Positive and Negative Cases -- 4 Experiments on Benchmarks -- 4.1 Experiment Setting and Evaluation Methodology -- 4.2 Congressional Voting Records -- 4.3 Tic Tac Toe Endgame -- 4.4 Cardiac Diagnosis -- 4.5 Car Evaluation -- 4.6 Results and Discussion -- 5 Conclusion -- References -- When Revision-Based Case Adaptation Meets Analogical Extrapolation -- 1 Introduction -- 2 Setting of the Problem and Running Example -- 2.1 A Quick Refresher About Propositional Logic -- 2.2 Notions and Notations Related to CBR -- 2.3 Specification of the Running Example -- 2.4 Analogical Proportions and CBR -- 2.5 Belief Revision and CBR -- 3 Bridging Extrapolation and Revision-Based Adaptation -- 3.1 Reformulating Adaptation by Extrapolation as a Single Case Adaptation.
3.2 A Revision Operator Based on Competence of Case Pairs -- 3.3 An Approach to Adaptation Based on Extrapolation and Revision -- 3.4 Synthesis -- 4 Related Work and Final Remarks -- References -- Inferring Case-Based Reasoners' Knowledge to Enhance Interactivity -- 1 Introduction -- 2 Problem Statement: Interaction with a CBR Agent -- 3 Modeling the User as a Case-Based Reasoner -- 4 Inference of the CBR Parameters -- 4.1 General Principle -- 4.2 Inference of the Parameters for a Deterministic CBR -- 4.3 Probability of Retrieval for kNN -- 4.4 Discussion on the Inference Process -- 5 Application: Teaching Word Inflection -- 5.1 Presentation of the Application -- 5.2 Implementation of a Case-Based Reasoning Learner -- 5.3 Empirical Evaluation -- 6 Conclusion -- References -- A Case-Based Approach for the Selection of Explanation Algorithms in Image Classification -- 1 Introduction -- 2 Background -- 2.1 LIME -- 2.2 Anchors -- 2.3 Integrated Gradients -- 2.4 XRAI -- 3 CBR Process Specification -- 3.1 Case Base Elicitation -- 3.2 Similarity Metrics -- 3.3 Reuse Strategies -- 4 Evaluation -- 5 Conclusions and Future Work -- References -- Towards Richer Realizations of Holographic CBR -- 1 Introduction -- 2 Holographic Case-Based Reasoning -- 3 Methodology -- 3.1 Key Ideas -- 3.2 Holographic CBR Realization Framework -- 4 Results and Interpretations -- 4.1 Comparison with Baseline -- 4.2 Tests for Efficiency -- 5 Conclusions and Future Directions -- References -- Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future -- 1 Introduction -- 1.1 The Problem: Grass Growth Prediction for Sustainable Dairy Farming -- 1.2 Related Work: Counterfactuals from XAI to Data Augmentation -- 1.3 Research Questions and Novelties.
2 Study 1: Predicting Climate Disruption with PBI-CBR -- 2.1 Defining a Class Boundary for Climate Outlier Cases -- 2.2 Experiment 1a: The Contribution of Climate Outliers to Predictions -- 2.3 Experiment 1b: Role of Training Outliers at Values of k -- 3 Study 2: Predicting Climate Disruption with Counterfactuals -- 3.1 A Case-Based Counterfactual Augmentation Algorithm (CFA) -- 3.2 Experiment 2: Using Synthetic Counterfactual Cases to Predict Growth -- 4 Conclusions: Novelties, Explications and Caveats -- References -- A Case-Based Approach to Data-to-Text Generation -- 1 Introduction -- 2 Related Works -- 3 Background -- 4 Methodology -- 4.1 Case-Base Creation -- 4.2 Retrieval and Feature Weighting -- 4.3 Generation -- 5 Experimental Setup -- 5.1 Dataset -- 5.2 Baseline and Benchmark -- 5.3 Evaluation Methods -- 6 Results and Discussion -- 6.1 Comparison with Benchmark and Baseline -- 6.2 Ablation Studies -- 6.3 Qualitative Analysis -- 7 Conclusion and Future Work -- References -- On Combining Knowledge-Engineered and Network-Extracted Features for Retrieval -- 1 Introduction -- 2 Convolutional Neural Networks for Classification -- 3 Related Work -- 4 Bridging Engineered and Network-Extracted Features -- 5 Evaluation -- 5.1 Test Domain and Testbed System -- 5.2 Preliminary Experiments to Set Network Parameters -- 5.3 How Retrieval Accuracy Changes with KE Feature Degradation -- 5.4 How Using KE and NL Features in Concert Affects Accuracy -- 5.5 How Learned Weights Further Influence Retrieval Accuracy -- 6 Ramifications for Explainability -- 7 Conclusions -- References -- Task and Situation Structures for Case-Based Planning -- 1 Introduction -- 2 Task Structure and Planning -- 2.1 Task Structure -- 2.2 Execution of Tasks -- 2.3 Implementation of Our Task Structure -- 2.4 Discussions on Task Structure -- 3 Situation Structure -- 4 Situation Handling.
5 Illustrative Examples.
Record Nr. UNINA-9910502645803321
Sánchez-Ruiz Antonio A.  
Cham, Switzerland : , : Springer International Publishing, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Case-based reasoning research and development : 29th International Conference, ICCBR 2021, Salamanca, Spain, September 13-16, 2021 : proceedings / / Antonio A. Sánchez-Ruiz, Michael W. Floyd
Case-based reasoning research and development : 29th International Conference, ICCBR 2021, Salamanca, Spain, September 13-16, 2021 : proceedings / / Antonio A. Sánchez-Ruiz, Michael W. Floyd
Autore Sánchez-Ruiz Antonio A.
Pubbl/distr/stampa Cham, Switzerland : , : Springer International Publishing, , [2021]
Descrizione fisica 1 online resource (337 pages)
Disciplina 006.33
Collana Lecture Notes in Computer Science
Soggetto topico Expert systems (Computer science)
Case-based reasoning
ISBN 3-030-86957-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- The Bites Eclectic: Critique-Based Conversational Recommendation for Diversity-Focused Meal Planning -- 1 Introduction -- 2 Background -- 2.1 Diversity -- 2.2 Critiquing -- 2.3 Recipe Recommendation and Diversity -- 3 DiversityBite: A Conversational Dynamic Critique-Based Recommender -- 3.1 Recipe Case Representation and Similarity -- 3.2 Diversity-Focused Conversational Critique -- 4 Evaluation for DiversityBite -- 4.1 Recipe Dataset -- 4.2 Evaluation Study -- 4.3 Evaluation Results -- 5 Discussion and Future Work -- References -- Evaluation of Similarity Measures for Flight Simulator Training Scenarios -- 1 Introduction -- 2 Related Work -- 3 Problem Definition: Pilot Competencies and Event Sets -- 4 Case Retrieval of Scenario Event Sets -- 5 Evaluation of Similarity Measures -- 5.1 Experimental Setup -- 5.2 Analysis -- 6 Results and Discussion -- 7 Conclusions -- References -- Instance-Based Counterfactual Explanations for Time Series Classification -- 1 Introduction -- 2 Related Work -- 3 Good Counterfactuals for Time Series: Key Properties -- 4 Native Guide: Counterfactual XAI for Time Series -- 5 Testing Native Guide: Two Comparative Experiments -- 5.1 Experiment 1: Probing Proximity and Sparsity -- 5.2 Experiment 2: Exploring Plausibility and Diversity -- 6 Conclusion and Future Directions -- References -- User Evaluation to Measure the Perception of Similarity Measures in Artworks -- 1 Introduction -- 2 Related Work About Similarity -- 3 Methodology for Learning Similarity Measures Reflecting Human Perceptions -- 3.1 Methodology for Learning Perception Aware Similarity Measures -- 3.2 Profile Definition -- 4 Experiment on the Perception of Similarity for Artworks -- 4.1 Definition of Local Similarity Measures -- 4.2 Data Gathering of Perceived Similarity -- 4.3 Experimental Results.
5 Conclusions and Future Work -- References -- Measuring Financial Time Series Similarity with a View to Identifying Profitable Stock Market Opportunities -- 1 Introduction -- 2 Related Work -- 3 From Prices to Cases -- 4 Similarity in Financial Time Series -- 4.1 The Problem with Correlation -- 4.2 An Adjusted Correlation Metric -- 4.3 A Novel Similarity Metric for Returns-Based Time-Series -- 4.4 Most and Least Similar Cases -- 5 Evaluation -- 5.1 Predicting Monthly Returns -- 5.2 Comparing Trading Strategies -- 6 Conclusion and Future Work -- References -- A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries -- 1 Introduction -- 2 Related Work -- 3 CBR for Injury Prediction and Explanation -- 3.1 Representing Training Load -- 3.2 Representing Injury Cases -- 3.3 Balancing the Case Base -- 3.4 Task 1: Predicting Training Breaks -- 3.5 Task 2: Explaining Training Breaks -- 4 Evaluation -- 4.1 Setup -- 4.2 Evaluating Prediction Accuracy -- 4.3 Evaluating Injury Explanations -- 5 Conclusions -- References -- Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists -- 1 Introduction -- 2 Related Work -- 3 Case and Problem Definition -- 4 BCBR Framework -- 4.1 Bayesian Inference -- 4.2 Case Base Creation and CBR Engine -- 4.3 Example: NBI Estimates, Case Retrieval and CBR Case -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experiment 1: Answer Classification Performance (Baselines) -- 5.3 Experiment 2: Trustworthiness of Constructed Checklists -- 5.4 Experiment 3: Evaluation of Constructed Checklists -- 6 Conclusion -- References -- Revisiting Fast and Slow Thinking in Case-Based Reasoning -- 1 Introduction -- 2 Background -- 3 Feature Selection -- 3.1 Feature Selection for Parsimonious Search -- 3.2 Modelling Desirability of Feature Sets -- 3.3 Brute Force Approach -- 3.4 Greedy Approach.
3.5 Experimental Analysis -- 4 Constraint-Based Switching -- 4.1 Model 2D -- 4.2 Experimental Analysis -- 5 Complexity Measure for Dichotomous Models -- 5.1 Footprint Size -- 5.2 Footprint with Time -- 5.3 Experimental Analysis -- 6 Conclusion and Future Work -- References -- Harmonizing Case Retrieval and Adaptation with Alternating Optimization -- 1 Introduction -- 2 Background -- 3 Alternating Optimization of Retrieval and Adaptation -- 4 Testbed System Design -- 4.1 Loss Function -- 4.2 Testbed Retrieval and Adaptation -- 4.3 Testbed Training and Testing Procedures -- 5 Evaluation -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 6 Guidelines for Applying AO to Train CBR Components -- 7 Future Work -- 8 Conclusion -- References -- Adaptation Knowledge Discovery Using Positive and Negative Cases -- 1 Introduction -- 2 Motivations and Preliminaries -- 2.1 Assumptions and Notations About CBR -- 2.2 Boolean Setting Illustrated with a Boolean Function Example -- 2.3 Itemset Extraction -- 3 Exploiting Case Variations for Adaptation Knowledge Discovery with Positive and Negative Cases -- 4 Experiments on Benchmarks -- 4.1 Experiment Setting and Evaluation Methodology -- 4.2 Congressional Voting Records -- 4.3 Tic Tac Toe Endgame -- 4.4 Cardiac Diagnosis -- 4.5 Car Evaluation -- 4.6 Results and Discussion -- 5 Conclusion -- References -- When Revision-Based Case Adaptation Meets Analogical Extrapolation -- 1 Introduction -- 2 Setting of the Problem and Running Example -- 2.1 A Quick Refresher About Propositional Logic -- 2.2 Notions and Notations Related to CBR -- 2.3 Specification of the Running Example -- 2.4 Analogical Proportions and CBR -- 2.5 Belief Revision and CBR -- 3 Bridging Extrapolation and Revision-Based Adaptation -- 3.1 Reformulating Adaptation by Extrapolation as a Single Case Adaptation.
3.2 A Revision Operator Based on Competence of Case Pairs -- 3.3 An Approach to Adaptation Based on Extrapolation and Revision -- 3.4 Synthesis -- 4 Related Work and Final Remarks -- References -- Inferring Case-Based Reasoners' Knowledge to Enhance Interactivity -- 1 Introduction -- 2 Problem Statement: Interaction with a CBR Agent -- 3 Modeling the User as a Case-Based Reasoner -- 4 Inference of the CBR Parameters -- 4.1 General Principle -- 4.2 Inference of the Parameters for a Deterministic CBR -- 4.3 Probability of Retrieval for kNN -- 4.4 Discussion on the Inference Process -- 5 Application: Teaching Word Inflection -- 5.1 Presentation of the Application -- 5.2 Implementation of a Case-Based Reasoning Learner -- 5.3 Empirical Evaluation -- 6 Conclusion -- References -- A Case-Based Approach for the Selection of Explanation Algorithms in Image Classification -- 1 Introduction -- 2 Background -- 2.1 LIME -- 2.2 Anchors -- 2.3 Integrated Gradients -- 2.4 XRAI -- 3 CBR Process Specification -- 3.1 Case Base Elicitation -- 3.2 Similarity Metrics -- 3.3 Reuse Strategies -- 4 Evaluation -- 5 Conclusions and Future Work -- References -- Towards Richer Realizations of Holographic CBR -- 1 Introduction -- 2 Holographic Case-Based Reasoning -- 3 Methodology -- 3.1 Key Ideas -- 3.2 Holographic CBR Realization Framework -- 4 Results and Interpretations -- 4.1 Comparison with Baseline -- 4.2 Tests for Efficiency -- 5 Conclusions and Future Directions -- References -- Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future -- 1 Introduction -- 1.1 The Problem: Grass Growth Prediction for Sustainable Dairy Farming -- 1.2 Related Work: Counterfactuals from XAI to Data Augmentation -- 1.3 Research Questions and Novelties.
2 Study 1: Predicting Climate Disruption with PBI-CBR -- 2.1 Defining a Class Boundary for Climate Outlier Cases -- 2.2 Experiment 1a: The Contribution of Climate Outliers to Predictions -- 2.3 Experiment 1b: Role of Training Outliers at Values of k -- 3 Study 2: Predicting Climate Disruption with Counterfactuals -- 3.1 A Case-Based Counterfactual Augmentation Algorithm (CFA) -- 3.2 Experiment 2: Using Synthetic Counterfactual Cases to Predict Growth -- 4 Conclusions: Novelties, Explications and Caveats -- References -- A Case-Based Approach to Data-to-Text Generation -- 1 Introduction -- 2 Related Works -- 3 Background -- 4 Methodology -- 4.1 Case-Base Creation -- 4.2 Retrieval and Feature Weighting -- 4.3 Generation -- 5 Experimental Setup -- 5.1 Dataset -- 5.2 Baseline and Benchmark -- 5.3 Evaluation Methods -- 6 Results and Discussion -- 6.1 Comparison with Benchmark and Baseline -- 6.2 Ablation Studies -- 6.3 Qualitative Analysis -- 7 Conclusion and Future Work -- References -- On Combining Knowledge-Engineered and Network-Extracted Features for Retrieval -- 1 Introduction -- 2 Convolutional Neural Networks for Classification -- 3 Related Work -- 4 Bridging Engineered and Network-Extracted Features -- 5 Evaluation -- 5.1 Test Domain and Testbed System -- 5.2 Preliminary Experiments to Set Network Parameters -- 5.3 How Retrieval Accuracy Changes with KE Feature Degradation -- 5.4 How Using KE and NL Features in Concert Affects Accuracy -- 5.5 How Learned Weights Further Influence Retrieval Accuracy -- 6 Ramifications for Explainability -- 7 Conclusions -- References -- Task and Situation Structures for Case-Based Planning -- 1 Introduction -- 2 Task Structure and Planning -- 2.1 Task Structure -- 2.2 Execution of Tasks -- 2.3 Implementation of Our Task Structure -- 2.4 Discussions on Task Structure -- 3 Situation Structure -- 4 Situation Handling.
5 Illustrative Examples.
Record Nr. UNISA-996464529403316
Sánchez-Ruiz Antonio A.  
Cham, Switzerland : , : Springer International Publishing, , [2021]
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Case-based reasoning research and development : 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, proceedings / / Mark T. Keane, Nirmalie Wiratunga (editors)
Case-based reasoning research and development : 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, proceedings / / Mark T. Keane, Nirmalie Wiratunga (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (420 pages)
Disciplina 153.43
Collana Lecture notes in computer science. Lecture notes in artificial intelligence
Soggetto topico Case-based reasoning
Expert systems (Computer science)
Deep learning (Machine learning)
ISBN 3-031-14923-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Invited Talks -- Seeing Through Black Boxes with Human Vision: Deep Learning and Explainable AI in Medical Image Applications -- Case-Based Reasoning for Clinical Decisions That Are Computer-Aided, Not Automated -- Towards More Cognitively Appealing Paradigms in Case-Based Reasoning -- Contents -- Explainability in CBR -- Using Case-Based Reasoning for Capturing Expert Knowledge on Explanation Methods -- 1 Introduction -- 2 Background -- 3 Case-Based Elicitation -- 3.1 Case Structure -- 3.2 Case Base Acquisition -- 4 CBR Process -- 5 Evaluation and Discussion -- 6 Conclusions -- References -- A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations -- 1 Introduction -- 2 Related Work -- 2.1 What Are Good Counterfactual Explanations? -- 2.2 Perturbation-Based Approaches -- 2.3 Instance-Based Approaches -- 2.4 Instance-Based Shortcomings -- 3 Good Counterfactuals in Multi-class Domains -- 3.1 Reusing the kNN Explanation Cases -- 3.2 Validating Candidate Counterfactuals -- 3.3 Discussion -- 4 Evaluation -- 4.1 Methodology -- 4.2 Results -- 5 Conclusions -- References -- How Close Is Too Close? The Role of Feature Attributions in Discovering Counterfactual Explanations -- 1 Introduction -- 2 Related Work -- 3 DisCERN -- 3.1 Nearest-Unlike Neighbour -- 3.2 Feature Ordering by Feature Attribution -- 3.3 Substitution-Based Adaptation -- 3.4 Integrated Gradients for DisCERN -- 3.5 Bringing the NUN Closer -- 4 Evaluation -- 4.1 Datasets -- 4.2 Experiment Setup -- 4.3 Performance Measures for Counterfactual Explanations -- 5 Results -- 5.1 A Comparison of Feature Attribution Techniques -- 5.2 A Comparison of Counterfactual Discovery Algorithms -- 5.3 Impact of Bringing NUN Closer -- 6 Conclusions -- References -- Algorithmic Bias and Fairness in Case-Based Reasoning.
1 Introduction -- 2 Related Research -- 2.1 Bias in ML -- 2.2 Bias in CBR -- 2.3 Metric Learning -- 3 FairRet: Eliminating Bias with Metric Learning -- 3.1 Bias and The Similarity Knowledge Container -- 3.2 A Metric Learning Approach -- 3.3 Multi-objective Particle Swarm Optimization -- 4 Results -- 4.1 Dealing with Underestimation Bias -- 4.2 Outcome Distortion -- 4.3 Retrieval Overlap -- 5 Conclusions -- References -- "Better" Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI) -- 1 Introduction -- 2 Background: Computation and Psychology of Counterfactuals -- 2.1 User Studies of Counterfactual XAI: Mixed Results -- 3 Study 1: Plotting Counterfactuals that have Categoricals -- 3.1 Results and Discussion -- 4 Transforming Case-Based Counterfactuals, Categorically -- 4.1 Case-Based Counterfactual Methods: CB1-CF and CB2-CF -- 4.2 Counterfactuals with Categorical Transforms #1: Global Binning -- 4.3 Counterfactuals with Categorical Transforms #2: Local Direction -- 5 Study 2: Evaluating CAT-CF Methods -- 5.1 Method: Data and Procedure -- 5.2 Results and Discussion: Counterfactual Distance -- 6 Conclusions -- References -- Representation and Similarity -- Extracting Case Indices from Convolutional Neural Networks: A Comparative Study -- 1 Introduction -- 2 Potential Feature Extraction Points in cnns -- 3 Related Work -- 4 Three Structure-Based Feature Extraction Methods -- 4.1 Post-convolution Feature Extraction -- 4.2 Post-dense Feature Extraction -- 4.3 Multi-net Feature Extraction -- 5 Evaluation -- 5.1 Hypotheses -- 5.2 Test Domain and Test Set Selection -- 5.3 Testbed System -- 5.4 Accuracy Testing and Informal Upper Bound -- 6 Results and Discussion -- 6.1 Comparative Performance -- 6.2 Discussion -- 7 Ramifications for Interpretability.
8 Conclusions and Future Work -- References -- Exploring the Effect of Recipe Representation on Critique-Based Conversational Recommendation -- 1 Introduction -- 2 Background -- 2.1 Diversity in Recommender Systems -- 2.2 Critique-Based Conversational Recommender Systems -- 2.3 Diversity in Recipe Recommenders -- 3 DiversityBite Framework: Recommend, Review, Revise -- 3.1 Adaptive Diversity Goal Approach -- 4 Evaluation -- 4.1 Case Base -- 4.2 Implementation: DGF, AGD, and Diversity Scoring -- 4.3 Simulation Study: Incorporating Diversity in Critique -- 4.4 User Study: Comparing Different Recipe Representations -- 5 Conclusion -- References -- Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study -- 1 Introduction -- 2 Related Work -- 3 SupportPrim CBR System -- 3.1 Data -- 3.2 Case Representation and Similarity Modeling -- 3.3 Case Base and Similarity Population -- 4 Explanatory Case Base Visualizations -- 4.1 Accessing the CBR System's Model -- 4.2 Visualization of Retrievals -- 4.3 Visualization of the Similarity Scores for Individual Case Comparisons -- 5 Experiments -- 6 Discussion -- 7 Conclusion -- References -- Adapting Semantic Similarity Methods for Case-Based Reasoning in the Cloud -- 1 Introduction -- 2 Related Work -- 2.1 Clood CBR -- 2.2 Ontologies in CBR -- 2.3 Retrieval with Word Embedding -- 2.4 Serverless Function Benefits and Limitations -- 3 Semantic Similarity Metrics in a Microservices Architecture -- 3.1 Clood Similarity Functions Overview -- 3.2 Similarity Table -- 3.3 Word Embedding Based Similarity -- 3.4 Ontology-Based Similarity Measure -- 4 Implementation of Semantic Similarity Measures on Clood Framework -- 4.1 Word Embedding Similarity on Clood -- 4.2 Ontology-Based Similarity on Clood -- 5 Evaluation of Resource Impact -- 5.1 Experiment Setup -- 5.2 Result and Discussion.
6 Conclusion -- References -- Adaptation and Analogical Reasoning -- Case Adaptation with Neural Networks: Capabilities and Limitations -- 1 Introduction -- 2 Background -- 3 NN-CDH for both Classification and Regression -- 3.1 General Model of Case Adaptation -- 3.2 1-Hot/1-Cold Nominal Difference -- 3.3 Neural Network Structure of NN-CDH -- 3.4 Training and Adaptation Procedure -- 4 Evaluation -- 4.1 Systems Being Compared -- 4.2 Assembling Case Pairs for Training -- 4.3 Data Sets -- 4.4 Artificial Data Sets -- 5 Conclusion -- References -- A Deep Learning Approach to Solving Morphological Analogies -- 1 Introduction -- 2 The Problem of Morphological Analogy -- 3 Proposed Approach -- 3.1 Classification, Retrieval and Embedding Models -- 3.2 Training and Evaluation -- 4 Experiments -- 4.1 Data -- 4.2 Refining the Training Procedure -- 4.3 Performance Comparison with State of the Art Methods -- 4.4 Distance of the Expected Result -- 4.5 Case Analysis: Navajo and Georgian -- 5 Conclusion and Perspectives -- References -- Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer -- 1 Introduction -- 2 Reminder on Complexity-Based Analogy -- 2.1 Notations -- 2.2 Ordinal Analogical Principle: Complexity Definition -- 2.3 Ordinal Analogical Inference Algorithm -- 3 Theoretical Property of the Complexity Measure: Upper Bound -- 3.1 General Case -- 3.2 Binary Classification Case -- 4 Algorithmic Optimisation -- 4.1 Principle -- 4.2 Proposed Optimized Algorithm -- 5 Experimental Study -- 5.1 Computational Cost -- 5.2 Correlation Between Case Base Complexity and Performance -- 5.3 Correlation Between Complexity and Task Difficulty -- 6 Conclusion and Future Works -- References -- Graphs and Optimisation -- Case-Based Learning and Reasoning Using Layered Boundary Multigraphs -- 1 Introduction -- 2 Background and Related Work.
3 Boundary Graphs and Labeled Boundary Multigraphs -- 3.1 Boundary Graphs -- 3.2 Labeled Boundary Multigraphs -- 3.3 Discussion -- 4 Empirical Evaluation -- 4.1 Experimental Set-Up -- 4.2 Classical Benchmark Data Sets -- 4.3 Scaling Analysis -- 5 Conclusion -- References -- Particle Swarm Optimization in Small Case Bases for Software Effort Estimation -- 1 Introduction -- 2 Related Work -- 3 Software Effort Estimation of User Stories -- 4 CBR Approach -- 4.1 Case Representation -- 4.2 Similarity -- 4.3 Adaptation -- 4.4 Weight Optimization with PSO -- 5 Experiments -- 5.1 Experimental Data -- 5.2 Experiment 1 -- 5.3 Experiment 2 -- 5.4 Discussion of Results -- 6 Conclusion -- References -- MicroCBR: Case-Based Reasoning on Spatio-temporal Fault Knowledge Graph for Microservices Troubleshooting -- 1 Introduction -- 2 Related Work -- 3 Background and Motivation -- 3.1 Background with Basic Concepts -- 3.2 Motivation -- 4 Troubleshooting Framework -- 4.1 Framework Overview -- 4.2 Spatio-Temporal Fault Knowledge Graph -- 4.3 Fingerprinting the Fault -- 4.4 Case-Based Reasoning -- 5 Evaluation -- 5.1 Evaluation Setup -- 5.2 Q1. Comparative Experiments -- 5.3 Q2. Ablation Experiment -- 5.4 Q3. Efficiency Experiments -- 5.5 Q4. Case Studies and Learned Lessons -- 6 Conclusion -- References -- .26em plus .1em minus .1emGPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning -- 1 Introduction -- 2 Foundations and Related Work -- 2.1 Semantic Workflow Graph Representation -- 2.2 State-Space Search by Using A* -- 2.3 Related Work -- 3 AMonG: A*-Based Graph Matching on Graphic Processing Units -- 3.1 Overview and Components -- 3.2 Parallel Graph Matching -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Discussion and Further Considerations -- 5 Conclusion and Future Work.
References.
Record Nr. UNINA-9910586636203321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Case-based reasoning research and development : 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, proceedings / / Mark T. Keane, Nirmalie Wiratunga (editors)
Case-based reasoning research and development : 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, proceedings / / Mark T. Keane, Nirmalie Wiratunga (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (420 pages)
Disciplina 153.43
Collana Lecture notes in computer science. Lecture notes in artificial intelligence
Soggetto topico Case-based reasoning
Expert systems (Computer science)
Deep learning (Machine learning)
ISBN 3-031-14923-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Invited Talks -- Seeing Through Black Boxes with Human Vision: Deep Learning and Explainable AI in Medical Image Applications -- Case-Based Reasoning for Clinical Decisions That Are Computer-Aided, Not Automated -- Towards More Cognitively Appealing Paradigms in Case-Based Reasoning -- Contents -- Explainability in CBR -- Using Case-Based Reasoning for Capturing Expert Knowledge on Explanation Methods -- 1 Introduction -- 2 Background -- 3 Case-Based Elicitation -- 3.1 Case Structure -- 3.2 Case Base Acquisition -- 4 CBR Process -- 5 Evaluation and Discussion -- 6 Conclusions -- References -- A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations -- 1 Introduction -- 2 Related Work -- 2.1 What Are Good Counterfactual Explanations? -- 2.2 Perturbation-Based Approaches -- 2.3 Instance-Based Approaches -- 2.4 Instance-Based Shortcomings -- 3 Good Counterfactuals in Multi-class Domains -- 3.1 Reusing the kNN Explanation Cases -- 3.2 Validating Candidate Counterfactuals -- 3.3 Discussion -- 4 Evaluation -- 4.1 Methodology -- 4.2 Results -- 5 Conclusions -- References -- How Close Is Too Close? The Role of Feature Attributions in Discovering Counterfactual Explanations -- 1 Introduction -- 2 Related Work -- 3 DisCERN -- 3.1 Nearest-Unlike Neighbour -- 3.2 Feature Ordering by Feature Attribution -- 3.3 Substitution-Based Adaptation -- 3.4 Integrated Gradients for DisCERN -- 3.5 Bringing the NUN Closer -- 4 Evaluation -- 4.1 Datasets -- 4.2 Experiment Setup -- 4.3 Performance Measures for Counterfactual Explanations -- 5 Results -- 5.1 A Comparison of Feature Attribution Techniques -- 5.2 A Comparison of Counterfactual Discovery Algorithms -- 5.3 Impact of Bringing NUN Closer -- 6 Conclusions -- References -- Algorithmic Bias and Fairness in Case-Based Reasoning.
1 Introduction -- 2 Related Research -- 2.1 Bias in ML -- 2.2 Bias in CBR -- 2.3 Metric Learning -- 3 FairRet: Eliminating Bias with Metric Learning -- 3.1 Bias and The Similarity Knowledge Container -- 3.2 A Metric Learning Approach -- 3.3 Multi-objective Particle Swarm Optimization -- 4 Results -- 4.1 Dealing with Underestimation Bias -- 4.2 Outcome Distortion -- 4.3 Retrieval Overlap -- 5 Conclusions -- References -- "Better" Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI) -- 1 Introduction -- 2 Background: Computation and Psychology of Counterfactuals -- 2.1 User Studies of Counterfactual XAI: Mixed Results -- 3 Study 1: Plotting Counterfactuals that have Categoricals -- 3.1 Results and Discussion -- 4 Transforming Case-Based Counterfactuals, Categorically -- 4.1 Case-Based Counterfactual Methods: CB1-CF and CB2-CF -- 4.2 Counterfactuals with Categorical Transforms #1: Global Binning -- 4.3 Counterfactuals with Categorical Transforms #2: Local Direction -- 5 Study 2: Evaluating CAT-CF Methods -- 5.1 Method: Data and Procedure -- 5.2 Results and Discussion: Counterfactual Distance -- 6 Conclusions -- References -- Representation and Similarity -- Extracting Case Indices from Convolutional Neural Networks: A Comparative Study -- 1 Introduction -- 2 Potential Feature Extraction Points in cnns -- 3 Related Work -- 4 Three Structure-Based Feature Extraction Methods -- 4.1 Post-convolution Feature Extraction -- 4.2 Post-dense Feature Extraction -- 4.3 Multi-net Feature Extraction -- 5 Evaluation -- 5.1 Hypotheses -- 5.2 Test Domain and Test Set Selection -- 5.3 Testbed System -- 5.4 Accuracy Testing and Informal Upper Bound -- 6 Results and Discussion -- 6.1 Comparative Performance -- 6.2 Discussion -- 7 Ramifications for Interpretability.
8 Conclusions and Future Work -- References -- Exploring the Effect of Recipe Representation on Critique-Based Conversational Recommendation -- 1 Introduction -- 2 Background -- 2.1 Diversity in Recommender Systems -- 2.2 Critique-Based Conversational Recommender Systems -- 2.3 Diversity in Recipe Recommenders -- 3 DiversityBite Framework: Recommend, Review, Revise -- 3.1 Adaptive Diversity Goal Approach -- 4 Evaluation -- 4.1 Case Base -- 4.2 Implementation: DGF, AGD, and Diversity Scoring -- 4.3 Simulation Study: Incorporating Diversity in Critique -- 4.4 User Study: Comparing Different Recipe Representations -- 5 Conclusion -- References -- Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study -- 1 Introduction -- 2 Related Work -- 3 SupportPrim CBR System -- 3.1 Data -- 3.2 Case Representation and Similarity Modeling -- 3.3 Case Base and Similarity Population -- 4 Explanatory Case Base Visualizations -- 4.1 Accessing the CBR System's Model -- 4.2 Visualization of Retrievals -- 4.3 Visualization of the Similarity Scores for Individual Case Comparisons -- 5 Experiments -- 6 Discussion -- 7 Conclusion -- References -- Adapting Semantic Similarity Methods for Case-Based Reasoning in the Cloud -- 1 Introduction -- 2 Related Work -- 2.1 Clood CBR -- 2.2 Ontologies in CBR -- 2.3 Retrieval with Word Embedding -- 2.4 Serverless Function Benefits and Limitations -- 3 Semantic Similarity Metrics in a Microservices Architecture -- 3.1 Clood Similarity Functions Overview -- 3.2 Similarity Table -- 3.3 Word Embedding Based Similarity -- 3.4 Ontology-Based Similarity Measure -- 4 Implementation of Semantic Similarity Measures on Clood Framework -- 4.1 Word Embedding Similarity on Clood -- 4.2 Ontology-Based Similarity on Clood -- 5 Evaluation of Resource Impact -- 5.1 Experiment Setup -- 5.2 Result and Discussion.
6 Conclusion -- References -- Adaptation and Analogical Reasoning -- Case Adaptation with Neural Networks: Capabilities and Limitations -- 1 Introduction -- 2 Background -- 3 NN-CDH for both Classification and Regression -- 3.1 General Model of Case Adaptation -- 3.2 1-Hot/1-Cold Nominal Difference -- 3.3 Neural Network Structure of NN-CDH -- 3.4 Training and Adaptation Procedure -- 4 Evaluation -- 4.1 Systems Being Compared -- 4.2 Assembling Case Pairs for Training -- 4.3 Data Sets -- 4.4 Artificial Data Sets -- 5 Conclusion -- References -- A Deep Learning Approach to Solving Morphological Analogies -- 1 Introduction -- 2 The Problem of Morphological Analogy -- 3 Proposed Approach -- 3.1 Classification, Retrieval and Embedding Models -- 3.2 Training and Evaluation -- 4 Experiments -- 4.1 Data -- 4.2 Refining the Training Procedure -- 4.3 Performance Comparison with State of the Art Methods -- 4.4 Distance of the Expected Result -- 4.5 Case Analysis: Navajo and Georgian -- 5 Conclusion and Perspectives -- References -- Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer -- 1 Introduction -- 2 Reminder on Complexity-Based Analogy -- 2.1 Notations -- 2.2 Ordinal Analogical Principle: Complexity Definition -- 2.3 Ordinal Analogical Inference Algorithm -- 3 Theoretical Property of the Complexity Measure: Upper Bound -- 3.1 General Case -- 3.2 Binary Classification Case -- 4 Algorithmic Optimisation -- 4.1 Principle -- 4.2 Proposed Optimized Algorithm -- 5 Experimental Study -- 5.1 Computational Cost -- 5.2 Correlation Between Case Base Complexity and Performance -- 5.3 Correlation Between Complexity and Task Difficulty -- 6 Conclusion and Future Works -- References -- Graphs and Optimisation -- Case-Based Learning and Reasoning Using Layered Boundary Multigraphs -- 1 Introduction -- 2 Background and Related Work.
3 Boundary Graphs and Labeled Boundary Multigraphs -- 3.1 Boundary Graphs -- 3.2 Labeled Boundary Multigraphs -- 3.3 Discussion -- 4 Empirical Evaluation -- 4.1 Experimental Set-Up -- 4.2 Classical Benchmark Data Sets -- 4.3 Scaling Analysis -- 5 Conclusion -- References -- Particle Swarm Optimization in Small Case Bases for Software Effort Estimation -- 1 Introduction -- 2 Related Work -- 3 Software Effort Estimation of User Stories -- 4 CBR Approach -- 4.1 Case Representation -- 4.2 Similarity -- 4.3 Adaptation -- 4.4 Weight Optimization with PSO -- 5 Experiments -- 5.1 Experimental Data -- 5.2 Experiment 1 -- 5.3 Experiment 2 -- 5.4 Discussion of Results -- 6 Conclusion -- References -- MicroCBR: Case-Based Reasoning on Spatio-temporal Fault Knowledge Graph for Microservices Troubleshooting -- 1 Introduction -- 2 Related Work -- 3 Background and Motivation -- 3.1 Background with Basic Concepts -- 3.2 Motivation -- 4 Troubleshooting Framework -- 4.1 Framework Overview -- 4.2 Spatio-Temporal Fault Knowledge Graph -- 4.3 Fingerprinting the Fault -- 4.4 Case-Based Reasoning -- 5 Evaluation -- 5.1 Evaluation Setup -- 5.2 Q1. Comparative Experiments -- 5.3 Q2. Ablation Experiment -- 5.4 Q3. Efficiency Experiments -- 5.5 Q4. Case Studies and Learned Lessons -- 6 Conclusion -- References -- .26em plus .1em minus .1emGPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning -- 1 Introduction -- 2 Foundations and Related Work -- 2.1 Semantic Workflow Graph Representation -- 2.2 State-Space Search by Using A* -- 2.3 Related Work -- 3 AMonG: A*-Based Graph Matching on Graphic Processing Units -- 3.1 Overview and Components -- 3.2 Parallel Graph Matching -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Discussion and Further Considerations -- 5 Conclusion and Future Work.
References.
Record Nr. UNISA-996485668603316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Case-based reasoning research and development : 8th International Conference on Case-Based Reasoning, ICCBR 2009, Seattle, WA, USA, July 20-23, 2009 : proceedings / / Lorraine McGinty, David C. Wilson (eds.)
Case-based reasoning research and development : 8th International Conference on Case-Based Reasoning, ICCBR 2009, Seattle, WA, USA, July 20-23, 2009 : proceedings / / Lorraine McGinty, David C. Wilson (eds.)
Edizione [1st ed. 2009.]
Pubbl/distr/stampa Berlin, Germany : , : Springer, , [2009]
Descrizione fisica 1 online resource (536 p.)
Disciplina 006.333
Collana Lecture notes in computer science
Lecture notes in artificial intelligence
Soggetto topico Artificial intelligence
Case-based reasoning
Expert systems (Computer science)
ISBN 1-282-29797-X
9786612297977
3-642-02998-1
Classificazione DAT 706f
SS 4800
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Invited Talks -- We’re Wiser Together -- Black Swans, Gray Cygnets and Other Rare Birds -- Theoretical/Methodological Research Papers -- Case Retrieval Reuse Net (CR2N): An Architecture for Reuse of Textual Solutions -- Case-Based Reasoning in Transfer Learning -- Toward Modeling and Teaching Legal Case-Based Adaptation with Expert Examples -- Opportunistic Adaptation Knowledge Discovery -- Improving Reinforcement Learning by Using Case Based Heuristics -- Dimensions of Case-Based Reasoner Quality Management -- Belief Merging-Based Case Combination -- Maintenance by a Committee of Experts: The MACE Approach to Case-Base Maintenance -- The Good, the Bad and the Incorrectly Classified: Profiling Cases for Case-Base Editing -- An Active Approach to Automatic Case Generation -- Four Heads Are Better than One: Combining Suggestions for Case Adaptation -- Adaptation versus Retrieval Trade-Off Revisited: An Analysis of Boundary Conditions -- Boosting CBR Agents with Genetic Algorithms -- Using Meta-reasoning to Improve the Performance of Case-Based Planning -- Multi-level Abstractions and Multi-dimensional Retrieval of Cases with Time Series Features -- On Similarity Measures Based on a Refinement Lattice -- An Overview of the Deterministic Dynamic Associative Memory (DDAM) Model for Case Representation and Retrieval -- Robust Measures of Complexity in TCBR -- S-Learning: A Model-Free, Case-Based Algorithm for Robot Learning and Control -- Quality Enhancement Based on Reinforcement Learning and Feature Weighting for a Critiquing-Based Recommender -- Abstraction in Knowledge-Rich Models for Case-Based Planning -- A Scalable Noise Reduction Technique for Large Case-Based Systems -- Conceptual Neighborhoods for Retrieval in Case-Based Reasoning -- CBR Supports Decision Analysis with Uncertainty -- Constraint-Based Case-Based Planning Using Weighted MAX-SAT -- Applied Research Papers -- A Value Supplementation Method for Case Bases with Incomplete Information -- Efficiently Implementing Episodic Memory -- Integration of a Methodology for Cluster-Based Retrieval in jColibri -- Case-Based Collective Inference for Maritime Object Classification -- Case-Based Reasoning for Situation-Aware Ambient Intelligence: A Hospital Ward Evaluation Study -- Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning -- Case-Based Support for Forestry Decisions: How to See the Wood from the Trees -- A Case-Based Perspective on Social Web Search -- Determining Root Causes of Drilling Problems by Combining Cases and General Knowledge.
Record Nr. UNINA-9910484803903321
Berlin, Germany : , : Springer, , [2009]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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