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Analysis of Rare Categories

Part of the Cognitive Technologies Book Series

DOI: 10.1007/978-3-642-22813-1

ISBN: 978-3-642-22812-4

Publisher: Springer Berlin Heidelberg - Springer Journals

Oct 22, 2011

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Chapter 1Introduction

He, Jingrui

Pages: 1 –8

[Imbalanced data sets are prevalent in real applications, i.e., some classes occupy the majority of the data set, a.k.a., the majority classes; whereas the remaining classes only have a few examples, a.k.a., the minority classes or the rare categories.]
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Chapter 2Survey and Overview

He, Jingrui

Pages: 9 –16

[Rare category analysis is related to many research areas, including active learning, where the goal is to improve the classification performance with the fewest label requests to the labeling oracle; imbalanced classification, where the goal is to construct a classifier for imbalanced data sets...
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Chapter 3Rare Category Detection

He, Jingrui

Pages: 17 –74

[In this chapter, we focus on rare category detection, the first task in the supervised setting. In this task, we are given an unlabeled, imbalanced data set, which is often non-separable, and have access to a labeling oracle, which is able to give us the class label of any example with a fixed...
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Chapter 4Rare Category Characterization

He, Jingrui

Pages: 75 –97

[In Chapter 3,we have introduced various algorithms for rare category detection, which result in a set of labeled examples. Based on this labeled set, a natural follow-up step is rare category characterization, i.e., to characterize the minority classes in order to identify all the rare examples...
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Chapter 5Unsupervised Rare Category Analysis

He, Jingrui

Pages: 99 –123

[In this chapter, we focus on unsupervised rare category analysis, i.e., no label information is available in the learning process, and address the following two problems: (1) rare category selection, i.e., selecting a set of examples which are likely to come from the minority class; (2) feature...
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Chapter 6Conclusion and Future Directions

He, Jingrui

Pages: 125 –128

[Rare categories are of key importance in many real applications: although the occurrence of such examples is rare, their impact is significant. Applications of rare category analysis include: financial fraud detection, Medicare fraud detection, network intrusion detection, astronomy, spam image...

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