Improving bagging performance through multi-algorithm ensembles

Kuo Wei Hsu, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Bagging 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. Its advantages include an increased capability of handling small data sets, less sensitivity to noise or outliers, and a parallel structure for efficient implementations. However, it has been found to be less accurate than some other ensemble methods. In this paper, we propose an approach that improves bagging through the employment of multiple classification algorithms in ensembles. Our approach preserves the parallel structure of bagging and improves the accuracy of bagging. As a result, it unlocks the power and expands the user base of bagging.

Original languageEnglish (US)
Title of host publicationNew Frontiers in Applied Data Mining - PAKDD 2011 International Workshops, Revised Selected Papers
Number of pages12
StatePublished - Mar 7 2012
Event15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011 - Shenzhen, China
Duration: May 24 2011May 27 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7104 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011


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