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[null,null,"Titolo",null,"Advances in Intelligent Data Analysis XXII : 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part II / / edited by Ioanna Miliou, Nico Piatkowski, Panagiotis Papapetrou"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"9783031585531"]
[{"$nulls":4},"3031585534"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (286 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 14642"]
[null,null,"Disciplina",null,"519.5"]
[null,null,"Soggetti",null,"Database management"]
[{"$nulls":4},"Education - Data processing"]
[{"$nulls":4},"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Natural language processing (Computer science)"]
[{"$nulls":4},"Database Management System"]
[{"$nulls":4},"Computers and Education"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Natural Language Processing (NLP)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Temporal and Sequence Data -- Kernel Corrector LSTM -- 1 Introduction -- 2 Related Work -- 2.1 LSTM -- 2.2 Data-Centric Time Series Forecasting -- 2.3 Corrector LSTM -- 3 Kernel Corrector LSTM -- 3.1 Training -- 3.2 Data Correction -- 4 Experimental Setup -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 5 Results -- 5.1 Comparison with cLSTM -- 5.2 Comparison with LSTM -- 6 Conclusion -- References -- Unsupervised Representation Learning for Smart Transportation -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experiments -- 4.1 Vehicle Acceleration Datasets -- 4.2 Encoder Details -- 4.3 "]
[{"$nulls":4},"Hyperparameter Tuning -- 4.4 Evaluation -- 5 Results and Discussion -- 6 Conclusion -- References -- T-DANTE: Detecting Group Behaviour in Spatio-Temporal Trajectories Using Context Information -- 1 Introduction -- 2 Background -- 3 Material and Methods -- 3.1 Problem Formulation -- 3.2 Affinity Learning Network -- 3.3 Graph Community Detection -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics and Baselines -- 4.3 Results and Discussions -- 5 Conclusion -- References -- Statistical Learning -- Backward Inference in Probabilistic Regressor Chains with Distributional Constraints -- 1 Introduction -- 2 Related Work -- 3 Our Method: Metropolis-Hasting Regressor Chains -- 4 Experiments -- 5 Results and Discussion -- 6 Conclusion -- References -- Empirical Comparison Between Cross-Validation and Mutation-Validation in Model Selection -- 1 Introduction and Related Work -- 2 Methods and Experimental Setup -- 3 Results -- 4 Discussion and Conclusion -- References -- Amplified Contribution Analysis for Federated Learning -- 1 Introduction -- 2 Prerequisites and Related Work -- 2.1 Federated Learning -- 2.2 Contribution Measures for Federated Learning -- 3 Amplified Deletion Analytics."]
[{"$nulls":4},"3.1 Notation and Aggregation Masks -- 3.2 Leave-one-out and Alpha-Amplified Function -- 4 Experiments -- 4.1 Description -- 4.2 Results -- 4.3 Commentary -- 5 Conclusions -- References -- Data Mining -- Monitoring Concept Drift in Continuous Federated Learning Platforms -- 1 Introduction -- 2 Related Work -- 2.1 Error-Based Drift Detection -- 2.2 Data-Based Drift Detection -- 3 Methodology -- 3.1 Concept Drift in CFL Platforms -- 3.2 Drift Detection -- 4 Preliminary Evaluation -- 4.1 Experimental Setup -- 4.2 CFL Performance Under Concept Drift -- 4.3 Detecting First Occurrence of Concept Drift -- 4.4 Detecting Best Performance Under Concept Drift -- 5 Conclusion, Limitation, and Future Work -- References -- S+t-SNE - Bringing Dimensionality Reduction to Data Streams -- 1 Introduction -- 2 Related Work -- 3 Streaming T-SNE (S+t-SNE) -- 3.1 Problem One - When to Start -- 3.2 Problem Two - Reduction of Space Fingerprint -- 3.3 Handling Drift -- 4 Experiments -- 4.1 Results -- 5 Conclusion -- References -- -DBSCAN: Augmenting DBSCAN with Prior Knowledge -- 1 Introduction -- 2 Related Work -- 3 Implementation -- 4 Evaluation -- 4.1 Synthetic Datasets -- 4.2 Real World Datasets -- 5 Conclusion and Future Work -- References -- Putting Sense into Incomplete Heterogeneous Data with Hypergraph Clustering Analysis -- 1 Introduction -- 2 Related Work -- 3 Hypergraph-Based Clustering Analysis Method -- 3.1 Step I: Hypergraph Construction -- 3.2 Step II: Transformation to Simple Graph -- 3.3 Step III: Cluster Integration and Analysis -- 3.4 Step IV: Deriving KPIs to Analyze Performance -- 4 Evaluation in Industrial Use-Case -- 4.1 Step I: Hypergraph Construction -- 4.2 Step II: Transformation to Simple Graph -- 4.3 Step III: Cluster Integration and Analysis -- 4.4 Step IV: Deriving KPIs to Analyze Performance -- 5 Conclusion -- References -- Optimization."]
[{"$nulls":4},"Efficient Lookahead Decision Trees -- 1 Introduction -- 2 Related Work -- 3 Technical Background -- 3.1 Level-Two Specialized Algorithm -- 3.2 Level-Two Lookahead Information Gain -- 4 Less Greedy Decision Trees -- 5 Results -- 6 Conclusion -- References -- Learning Curve Extrapolation Methods Across Extrapolation Settings -- 1 Introduction -- 2 Related Work -- 3 Extrapolation Methods -- 4 Experimental Setup -- 5 Results and Discussion -- 6 Conclusion -- References -- Efficient NAS with FaDE on Hierarchical Spaces -- 1 Introduction -- 2 Fast DARTS Estimator -- 2.1 DARTS -- 2.2 Training a Chained Hierarchical Architecture Using DARTS -- 2.3 Deriving FaDE-Ranks on Hyper-Architecture -- 2.4 Joint Batch-Wise Pseudo Gradient Descent -- 3 Experiments -- 3.1 Validating FaDE-Ranks -- 3.2 NAS on Iterative "]
[{"$nulls":4},"FaDE-Ranks -- 4 Conclusion and Future Work -- References -- Investigating the Relation Between Problem Hardness and QUBO Properties -- 1 Introduction -- 2 Background -- 3 QUBO Formulations and Their Spectral Gaps -- 3.1 Kernel 2-Means Clustering -- 3.2 Simple Support Vector Machine Embedding -- 4 Experiments -- 4.1 Clustering -- 4.2 Support Vector Machine -- 5 Conclusion -- References -- XAI -- Example-Based Explanations of Random Forest Predictions -- 1 Introduction -- 2 Modifying the Prediction Procedure of Random Forests -- 2.1 Random Forests -- 2.2 Modifying the Predictions -- 3 Empirical Investigation -- 3.1 Observing the Effective Number of Training Examples -- 3.2 Controlling the Number of Examples Used in the Predictions -- 4 Concluding Remarks -- References -- FLocalX - Local to Global Fuzzy Explanations for Black Box Classifiers -- 1 Introduction -- 2 Setting the Stage -- 3 Fuzzy Local to Global Explanation Framework -- 3.1 Local to Global Fuzzy Set Transformation -- 3.2 Global Fuzzy Set Theory Encoding -- 3.3 Global Explanation Theory Generation."]
[{"$nulls":4},"4 Experiments -- 5 Conclusions and Future Work -- References -- Interpretable Quantile Regression by Optimal Decision Trees -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Quantile DL8.5 -- 4.1 Simultaneous Tree Learning -- 4.2 Efficient Quantile Loss Computation -- 5 Experiments -- 5.1 Metrics -- 5.2 Results -- 6 Conclusion -- References -- SLIPMAP: Fast and Robust Manifold Visualisation for Explainable AI -- 1 Introduction -- 2 Problem Definition -- 2.1 SLIPMAP -- 2.2 Mapping from Covariates to the Target Variable -- 3 Algorithm -- 3.1 Computational Complexity -- 4 Datasets -- 5 Experiments -- 5.1 Predictions -- 5.2 Robustness -- 5.3 Local Explanation Comparison -- 5.4 Scaling -- 6 Conclusions and Future Work -- References -- A Frank System for Co-Evolutionary Hybrid Decision-Making -- 1 Introduction -- 2 Setting the Stage -- 3 A Frank System -- 4 Experiments -- 5 Conclusion -- References -- Industrial Challenge -- Predicting the Failure of Component X in the Scania Dataset with Graph Neural Networks -- 1 Introduction -- 2 Methods -- 3 Exploratory Data Analysis -- 4 Data Preprocessing -- 5 Experiments -- 6 Conclusion -- References -- Towards Contextual, Cost-Efficient Predictive Maintenance in Heavy-Duty Trucks -- 1 Introduction -- 2 Problem Formulation -- 3 Methodology -- 3.1 Data Transformation -- 3.2 Predictive Model -- 4 Evaluation -- 5 Conclusion -- References -- Implementing Deep Learning Models for Imminent Component X Failures Prediction in Heavy-Duty Scania Trucks -- 1 Background -- 1.1 Predictive Maintenance -- 1.2 Contest Background Description -- 2 Analysis Method for Input Data Preparation -- 2.1 Exploring Raw Data -- 2.2 Preprocessing Raw Data -- 2.3 Preprocessing Train Labels -- 3 Modeling -- 3.1 Model Description -- 3.2 Summary of Model Performance -- 4 Reflection -- 5 Conclusion -- References -- Author Index."]
[null,null,null,"Sommario/riassunto","The two volume set LNCS 14641 and 14642 constitutes the proceedings of the 22nd International Symposium on Intelligent Data Analysis, IDA 2024, which was held in Stockholm, Sweden, during April 24-26, 2024. The 40 full and 3 short papers included in the proceedings were carefully reviewed and selected from 94 submissions. IDA is an international symposium presenting advances in the intelligent analysis of data. Distinguishing characteristics of IDA are its focus on novel, inspiring ideas, its focus on research, and its relatively small scale. ."]