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Improving bagging performance through multi-algorithm ensembles

Content Provider	Springer Nature Link
Author	Hsu, Kuo Wei Srivastava, Jaideep
Copyright Year	2012
Abstract	Working as an ensemble method that establishes a committee of classifiers first and then aggregates their outcomes through majority voting, bagging has attracted considerable research interest and been applied in various application domains. It has demonstrated several advantages, but in its present form, bagging has been found to be less accurate than some other ensemble methods. To unlock its power and expand its user base, we propose an approach that improves bagging through the use of multi-algorithm ensembles. In a multi-algorithm ensemble, multiple classification algorithms are employed. Starting from a study of the nature of diversity, we show that compared to using different training sets alone, using heterogeneous algorithms together with different training sets increases diversity in ensembles, and hence we provide a fundamental explanation for research utilizing heterogeneous algorithms. In addition, we partially address the problem of the relationship between diversity and accuracy by providing a non-linear function that describes the relationship between diversity and correlation. Furthermore, after realizing that the bootstrap procedure is the exclusive source of diversity in bagging, we use heterogeneity as another source of diversity and propose an approach utilizing heterogeneous algorithms in bagging. For evaluation, we consider several benchmark data sets from various application domains. The results indicate that, in terms of F1-measure, our approach outperforms most of the other state-of-the-art ensemble methods considered in experiments and, in terms of mean margin, our approach is superior to all the others considered in experiments.
Starting Page	498
Ending Page	512
Page Count	15
File Format	PDF
ISSN	20952228
Journal	Frontiers of Computer Science in China
Volume Number	6
Issue Number	5
e-ISSN	16737466
Language	English
Publisher	SP Higher Education Press
Publisher Date	2012-07-08
Publisher Institution	Chinese Universities
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	bagging classification diversity ensemble Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

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