NDLI: Theoretical Study of the Relationship between Diversity and Single-Class Measures for Class Imbalance Learning

Content Provider	IEEE Xplore Digital Library
Author	Shuo Wang Xin Yao
Copyright Year	2009
Abstract	This paper presents the theoretical research about the relationship between diversity of classification ensembles and single-class measures that are commonly used in class imbalance learning. Although there have been studies on diversity and its links to overall ensemble accuracy, little work has been done on the impact of diversity on single-class performance measures in class imbalance learning. The study of class imbalance learning is important, because many real-world problems, such as those in medical diagnosis, fraud detection, condition monitoring, etc., have imbalanced classes, where a minority class is usually more important and interesting than the majority class. In order to gain a deeper understanding of ensemble learning for imbalanced classes, this paper studies the impact of diversity on single-class performance measures theoretically and empirically. One of the main objectives of this paper is to find out if and when ensemble diversity can improve the classification performance on the important (minority) class.
Starting Page	76
Ending Page	81
File Size	796841
Page Count	6
File Format	PDF
ISBN	9781424453849
DOI	10.1109/ICDMW.2009.29
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Condition monitoring Costs Voting Conferences Training data Data mining Application software Medical diagnosis Computational intelligence
Content Type	Text
Resource Type	Article

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