© 2012

Analysis of Rare Categories


Part of the Cognitive Technologies book series (COGTECH)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Jingrui He
    Pages 1-8
  3. Jingrui He
    Pages 9-16
  4. Jingrui He
    Pages 17-74
  5. Jingrui He
    Pages 75-97
  6. Jingrui He
    Pages 99-123
  7. Jingrui He
    Pages 125-128
  8. Back Matter
    Pages 129-135

About this book


In many real-world problems, rare categories (minority classes) play essential roles despite their extreme scarcity. The discovery, characterization and prediction of rare categories of rare examples may protect us from fraudulent or malicious behavior, aid scientific discovery, and even save lives.

This book focuses on rare category analysis, where the majority classes have smooth distributions, and the minority classes exhibit the compactness property. Furthermore, it focuses on the challenging cases where the support regions of the majority and minority classes overlap. The author has developed effective algorithms with theoretical guarantees and good empirical results for the related techniques, and these are explained in detail. The book is suitable for researchers in the area of artificial intelligence, in particular machine learning and data mining.


Data analysis Data mining Feature selection Machine learning Majority class Minority class Rare category

Authors and affiliations

  1. 1., Machine Learning GroupIBM T.J. Watson Research CenterYorktown HeightsUSA

About the authors

Dr. Jingrui He received her PhD from Carnegie Mellon University. She is a researcher in the Machine Learning Group of the IBM T.J. Watson Research Center. Her research interests include rare category analysis, active learning, semisupervised learning, transfer learning and spam filtering.

Bibliographic information

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