Analysis of Rare Categories / by Jingrui He.

Format:

Book

Author/Creator:

He, Jingrui, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Cognitive technologies 1611-2482

Cognitive Technologies, 1611-2482

Language:

English

Subjects (All):

Artificial intelligence.

Data mining.

Computational intelligence.

Data structures (Computer science).

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Computational Intelligence.

Data Structures and Information Theory.

Local Subjects:

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Computational Intelligence.

Data Structures and Information Theory.

Physical Description:

1 online resource (VIII, 136 pages).

Edition:

First edition 2012.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.

System Details:

text file PDF

Summary:

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.

Contents:

Introduction

Survey and Overview

Rare Category Detection

Rare Category Characterization

Unsupervised Rare Category Analysis

Conclusion and Future Directions.

Other Format:

Printed edition:

ISBN:

978-3-642-22813-1

9783642228131

Access Restriction:

Restricted for use by site license.