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Learning from Imbalanced Data Sets

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Cover of 'Learning from Imbalanced Data Sets'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to KDD and Data Science
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    Chapter 2 Foundations on Imbalanced Classification
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    Chapter 3 Performance Measures
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    Chapter 4 Cost-Sensitive Learning
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    Chapter 5 Data Level Preprocessing Methods
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    Chapter 6 Algorithm-Level Approaches
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    Chapter 7 Ensemble Learning
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    Chapter 8 Imbalanced Classification with Multiple Classes
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    Chapter 9 Dimensionality Reduction for Imbalanced Learning
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    Chapter 10 Data Intrinsic Characteristics
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    Chapter 11 Learning from Imbalanced Data Streams
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    Chapter 12 Non-classical Imbalanced Classification Problems
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    Chapter 13 Imbalanced Classification for Big Data
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    Chapter 14 Software and Libraries for Imbalanced Classification
Attention for Chapter 7: Ensemble Learning
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