Overview
- First systematic investigation of rare categories
- Suitable for researchers in the areas of data mining and feature selection
- Develops effective algorithms with theoretical guarantees as well as good empirical results
- Includes supplementary material: sn.pub/extras
Part of the book series: Cognitive Technologies (COGTECH)
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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.
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Keywords
Table of contents (6 chapters)
Authors and Affiliations
About the author
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
Book Title: Analysis of Rare Categories
Authors: Jingrui He
Series Title: Cognitive Technologies
DOI: https://doi.org/10.1007/978-3-642-22813-1
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-22812-4Published: 05 January 2012
Softcover ISBN: 978-3-642-43075-6Published: 22 February 2014
eBook ISBN: 978-3-642-22813-1Published: 05 January 2012
Series ISSN: 1611-2482
Series E-ISSN: 2197-6635
Edition Number: 1
Number of Pages: VIII, 136
Topics: Artificial Intelligence, Data Mining and Knowledge Discovery, Computational Intelligence, Data Structures and Information Theory