TY - GEN
T1 - Improving bagging performance through multi-algorithm ensembles
AU - Hsu, Kuo Wei
AU - Srivastava, Jaideep
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84857737007&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-28320-8_40
DO - 10.1007/978-3-642-28320-8_40
M3 - Conference contribution
AN - SCOPUS:84857737007
SN - 9783642283192
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 471
EP - 482
BT - New Frontiers in Applied Data Mining - PAKDD 2011 International Workshops, Revised Selected Papers
T2 - 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011
Y2 - 24 May 2011 through 27 May 2011
ER -