Case-based reasoning : processes, suitability and applications / / Antonia M. Leeland, editor

• Record Nr.: UNINA9910974790403321
• Pubbl/distr/stampa: Hauppauge, N.Y., : Nova Science Publishers, c2011
• ISBN: 1-61728-814-4
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (183 p.)
• Collana: Engineering tools, techniques and tables
• Altri autori (Persone): LeelandAntonia M
• Disciplina: 153.4/3
• Soggetti: Case-based reasoning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- CASE-BASED REASONING: PROCESSES, SUITABILITY AND APPLICATIONS -- CASE-BASED REASONING: PROCESSES, SUITABILITY AND APPLICATIONS -- LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA -- CONTENTS -- PREFACE -- Chapter 1 CASE-BASED REASONING INTEGRATIONS: APPROACHES AND APPLICATIONS -- ABSTRACT -- 1. INTRODUCTION -- 2. TRENDS IN INTEGRATIONS OF CBR WITH OTHER INTELLIGENT METHODS -- 3. REPRESENTATIVE SYSTEMS -- 3.1 Sequential Processing Approaches -- 3.1.1 Loosely coupled sequence -- 3.1.2 Tightly coupled sequence -- 3.2. Co-processing Approaches -- 3.2.1 Cooperation oriented -- 3.2.1.1 Explicit reasoning control -- 3.2.1.2 Implicit reasoning control -- 3.2.2 Reconciliation oriented -- 3.3 Embedded Processing -- 4. COMBINATION OF CBR WITH NEURULES -- 4.1 Syntax and Semantics -- 4.2 Indexing and Hybrid Inference -- CONCLUSIONS -- REFERENCES -- Chapter 2 APPLYING IMPROVED CASE INDEXING AND RETRIEVING USING EX-POST INFORMATION IN CORPORATE BANKRUPTCY PREDICTION -- ABSTRACT -- INTRODUCTION -- LITERATURE REVIEW -- Distance Metric -- 1. Linear distance metrics -- 2. Value difference metric -- Feature selection &amp -- determining number of cases -- Weighting features -- PROPOSED MODEL -- RESEARCH DATA AND EXPERIMENTS -- Step 1. Selecting Observation Firm Set -- Step 2. Categorizing Financial Dimensions -- Step 3. Identifying and Obtaining Candidate Financial Ratios -- Step 4. Selecting Final Financial Ratios -- Step 5. Calculating Efficiencies Using DEA for the Data Set -- Step 6. ; Determining Case Base -- Step 7. Dividing Experiment Sets -- Step 8. Calculating Feature Weights -- Step 9. Measuring Similarity -- Step 10. Updating Case Base -- RESULT AND ANALYSIS -- Experiment 1 -- Experiment 2 -- Unsupervised vs. Supervised -- CONCLUSION -- REFERENCES -- Chapter 3 CASE-BASED REASONING: HISTORY, METHODOLOGY AND DEVELOPMENT TRENDS -- ABSTRACT.; INTRODUCTION -- HISTORY OF CBR -- THE STEPS OF THE CBR PROCESS -- MAIN TYPES OF CBR METHODS -- Case-Based Reasoning -- Analogy-Based Reasoning -- Exemplar-Based Reasoning -- Instance-Based Reasoning -- Memory-Based Reasoning -- 5. TOOLS AND APPLICATIONS OF CBR -- 6. DEVELOPMENT TRENDS OF CBR METHODS AND APPLICATIONS -- CONCLUSION -- REFERENCES -- Chapter 4 A TEMPORAL CASE-BASED PROCEDURE FOR CANCELLATION FORECASTING: A CASE STUDY -- ABSTRACT -- 1. INTRODUCTION -- 2. CANCELLATION CURVES -- 2.1. Canceling Patterns before Departure -- 2.2. Cancellation Patterns at Departure -- 3. MODELS -- 3.1. Case-Based Predicting Model (CBP) -- 3.1.1. Similarity evaluation -- 3.1.2. Sample selection -- 3.1.3. Prediction generation -- 3.1.4. Parameter search -- 3.2. Regression Models -- 3.3. Pick up Models -- 4. EMPIRICAL STUDY -- 4.1. The Best Number of Selection -- 4.2. Comparison with a Naïve CBP Variant -- 4.3. Comparison with Four Benchmarks -- 4.4. Distributions of the Estimated Parameters -- CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- Chapter 5 PROVISION OF SAFETY FOR TECHNOLOGICAL SYSTEMS WITH THE AID OF CASE-BASED REASONING -- ABSTRACT -- 1. INTRODUCTION -- 2. CONCEPTUALIZATION OF DATA AND KNOWLEDGE -- 3. THE CASE-BASED APPROACH -- 4. IMPLEMENTATION OF THE SOFTWARE -- 5. EXAMPLE OF APPLICATION OF THE SOFTWARE -- CONCLUSION -- REFERENCES -- Chapter 6 MATHEMATIZING THE CASE-BASED REASONING PROCESS -- ABSTRACT -- INTRODUCTION -- THE MARKOV MODEL -- MEASURING THE EFFECTIVENESS OF A CBR SYSTEM -- FUZZY SETS -- A FUZZY MODEL FOR THE REPRESENTATION OF A CBR SYSTEM -- AN APPLICATION OF THE FUZZY MODEL -- CONCLUSION -- REFERENCES -- Chapter 7 PROTOTYPE-BASED REASONING FOR DIAGNOSIS OF DYSMORPHIC SYNDROMES -- ABSTRACT -- 1. INTRODUCTION -- 1.1. Diagnostic Support for Dysmorphic Syndromes -- 1.2. Other Systems.; 1.3. Case-Based Reasoning and Prototypicality Measures -- 2. DIAGNOSIS OF DYSMORPHIC SYNDROMES -- 2.1. Prototypicality Measures -- 2.2. Adaptation Rules -- 3. RESULTS -- 3.1. Application of Adaptation Rules -- 3.2. Application of Adaptation Rules -- 3.3. Application of Automatically Acquired Adaptation Rules -- 4. CONCLUSION -- REFERENCES -- Chapter 8 NEW APPROACH OF CASE-BASED REASONING* -- ABSTRACT -- 1. INTRODUCTION -- 2. NEGOTIATION -- 3. DESCRIPTION OF OUR APPROACH -- 3.1. The 3R Model -- Retrieve -- Reuse -- Retain -- 3.2. Real Estate Negotiation According to the 3R Model -- Case -- Case base -- 3.3. The 3R Model Cycle -- 3.3.1. Retrieve -- A) Retrieve 1: Optimal weights search -- B) Optimal weights specification -- B-1) Initialization of the weights -- B-2) Calculation of the similarity distance -- B-3) Adjustment of the weights -- B-4) Calculation of the optimal weights -- B) Retrieve 2: The search for the similar case -- A) Computation of the Similarity Distance in Relation to the Target -- B) Similar Case Retrieval -- 3.3.3. Retain -- 4. MODEL VALIDATION -- 5. CONCLUSION -- REFERENCES -- Commentary FUZZY SETS IN CASE-BASED REASONING -- INDEX.
• Sommario/riassunto: Case-based reasoning (CBR) is the process of solving new problems based on the solutions of similar past problems. This book presents research in the field of CBR including business predication researches ; of corporate failure using CBR, and mathematising the Case-Based Reasoning process.