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Table of Contents
Preface; Contents; 1 Introduction; 1.1 The Knowledge Discovery Process; 1.2 Preprocessing; 1.2.1 Data Preparation; 1.2.2 Data Reduction; 1.3 Data Mining; 1.3.1 Supervised Learning; 1.3.2 Unsupervised Learning; 1.3.3 Semi-supervised Learning; 1.3.4 Scalability Consideration; 1.4 Classification; 1.4.1 Validation Schemes; 1.4.2 Evaluation Measures; References; 2 Multiple Instance Learning; 2.1 Formal Description; 2.2 Origin of MIL; 2.2.1 Relationship with Propositional Learning; 2.2.2 Relationship with Relational Learning ; 2.3 MIL Paradigms; 2.3.1 Multi-instance Classification and Regression.
2.3.2 Multi-instance Clustering2.3.3 Instance Annotation; 2.4 Applications of MIL; 2.4.1 Bioinformatics; 2.4.2 Image Classification and Retrieval; 2.4.3 Web Mining and Text Classification; 2.4.4 Object Detection and Tracking; 2.4.5 Medical Diagnosis and Imaging; 2.4.6 Other Classification Applications; 2.4.7 Regression Applications; 2.4.8 Clustering Applications; References; 3 Multi-instance Classification; 3.1 Introduction; 3.2 Formal Description; 3.3 Taxonomy ; 3.4 MI Assumptions ; 3.4.1 Standard MI Assumption; 3.4.2 Weidmann et al.'s Hierarchy; 3.4.3 Collective Assumption.
3.4.4 Mixture Distribution Assumption3.4.5 Soft Bag MI Assumption; 3.5 Distance Metrics ; 3.5.1 Bags as Point Sets; 3.5.2 Bags as Probability Distributions; 3.6 Real-World Applications ; 3.6.1 Bioinformatics; 3.6.2 Image Classification and Retrieval; 3.6.3 Web Mining and Text Classification; 3.6.4 Medical Diagnosis and Imaging; 3.6.5 Acoustic Classification; 3.7 Some Comments on Software Tools ; References; 4 Instance-Based Classification Methods; 4.1 Introduction ; 4.2 Wrapper Methods to Single-Instance Learning Algorithms; 4.3 Maximum Likelihood-Based Methods.
4.3.1 Maximum Likelihood Principle4.3.2 Diverse Density; 4.3.3 Logistic Regression; 4.3.4 Boosting; 4.4 Decision Rules and Tree-Based Methods ; 4.5 Instance-Level SVM; 4.6 Neural Network-Based Methods ; 4.6.1 Feedforward Neural Networks; 4.6.2 Recurrent Neural Networks; 4.6.3 Decision-Based Neural Networks; 4.6.4 Network Combinations; 4.7 Evolutionary Based Methods ; 4.8 Experimental Analysis ; 4.8.1 Setup; 4.8.2 Results and Discussion; 4.9 Summarizing Comments; References; 5 Bag-Based Classification Methods; 5.1 Introduction; 5.2 Original Bag Space Methods; 5.2.1 Nearest Neighbor Methods.
5.2.2 Bag-Level SVM5.3 Mapped Bag Space Methods; 5.3.1 Mapping Methods Based on Bag Statistics; 5.3.2 Mapping Methods Based on Prototype Concatenation ; 5.3.3 Mapping Methods Based on Counting; 5.3.4 Mapping Methods Based on Distance; 5.3.5 Bag-Level Distance Mapping Methods; 5.4 Experimental Analysis ; 5.4.1 Setup; 5.4.2 Results and Discussion; 5.5 Comparing Instance-Based, Bag-Based, and Traditional Classification Methods; 5.6 Summarizing Comments; References; 6 Multi-instance Regression; 6.1 Introduction ; 6.2 MIR Formulation ; 6.2.1 Problem Description ; 6.2.2 Evaluation Measures.
2.3.2 Multi-instance Clustering2.3.3 Instance Annotation; 2.4 Applications of MIL; 2.4.1 Bioinformatics; 2.4.2 Image Classification and Retrieval; 2.4.3 Web Mining and Text Classification; 2.4.4 Object Detection and Tracking; 2.4.5 Medical Diagnosis and Imaging; 2.4.6 Other Classification Applications; 2.4.7 Regression Applications; 2.4.8 Clustering Applications; References; 3 Multi-instance Classification; 3.1 Introduction; 3.2 Formal Description; 3.3 Taxonomy ; 3.4 MI Assumptions ; 3.4.1 Standard MI Assumption; 3.4.2 Weidmann et al.'s Hierarchy; 3.4.3 Collective Assumption.
3.4.4 Mixture Distribution Assumption3.4.5 Soft Bag MI Assumption; 3.5 Distance Metrics ; 3.5.1 Bags as Point Sets; 3.5.2 Bags as Probability Distributions; 3.6 Real-World Applications ; 3.6.1 Bioinformatics; 3.6.2 Image Classification and Retrieval; 3.6.3 Web Mining and Text Classification; 3.6.4 Medical Diagnosis and Imaging; 3.6.5 Acoustic Classification; 3.7 Some Comments on Software Tools ; References; 4 Instance-Based Classification Methods; 4.1 Introduction ; 4.2 Wrapper Methods to Single-Instance Learning Algorithms; 4.3 Maximum Likelihood-Based Methods.
4.3.1 Maximum Likelihood Principle4.3.2 Diverse Density; 4.3.3 Logistic Regression; 4.3.4 Boosting; 4.4 Decision Rules and Tree-Based Methods ; 4.5 Instance-Level SVM; 4.6 Neural Network-Based Methods ; 4.6.1 Feedforward Neural Networks; 4.6.2 Recurrent Neural Networks; 4.6.3 Decision-Based Neural Networks; 4.6.4 Network Combinations; 4.7 Evolutionary Based Methods ; 4.8 Experimental Analysis ; 4.8.1 Setup; 4.8.2 Results and Discussion; 4.9 Summarizing Comments; References; 5 Bag-Based Classification Methods; 5.1 Introduction; 5.2 Original Bag Space Methods; 5.2.1 Nearest Neighbor Methods.
5.2.2 Bag-Level SVM5.3 Mapped Bag Space Methods; 5.3.1 Mapping Methods Based on Bag Statistics; 5.3.2 Mapping Methods Based on Prototype Concatenation ; 5.3.3 Mapping Methods Based on Counting; 5.3.4 Mapping Methods Based on Distance; 5.3.5 Bag-Level Distance Mapping Methods; 5.4 Experimental Analysis ; 5.4.1 Setup; 5.4.2 Results and Discussion; 5.5 Comparing Instance-Based, Bag-Based, and Traditional Classification Methods; 5.6 Summarizing Comments; References; 6 Multi-instance Regression; 6.1 Introduction ; 6.2 MIR Formulation ; 6.2.1 Problem Description ; 6.2.2 Evaluation Measures.