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Constructing better classifier ensemble based on weighted accuracy and diversity measure
Zeng X.; Wong D.F.; Chao L.S.
2014-03-06
Source PublicationThe Scientific World Journal
ISSN1537744X
Volume2014
Abstract

A weighted accuracy and diversity (WAD) method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only be accurate but also different from every other member. In fact, accuracy and diversity are mutual restraint factors; that is, an ensemble with high accuracy may have low diversity, and an overly diverse ensemble may negatively affect accuracy. This study proposes a method to find the balance between accuracy and diversity that enhances the predictive ability of an ensemble for unknown data. The quality assessment for an ensemble is performed such that the final score is achieved by computing the harmonic mean of accuracy and diversity, where two weight parameters are used to balance them. The measure is compared to two representative measures, Kappa-Error and GenDiv, and two threshold measures that consider only accuracy or diversity, with two heuristic search algorithms, genetic algorithm, and forward hill-climbing algorithm, in ensemble selection tasks performed on 15 UCI benchmark datasets. The empirical results demonstrate that the WAD measure is superior to others in most cases. © 2014 Xiaodong Zeng et al.

DOI10.1155/2014/961747
URLView the original
Indexed BySCI
Language英语
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000330891600001
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Zeng X.,Wong D.F.,Chao L.S.. Constructing better classifier ensemble based on weighted accuracy and diversity measure[J]. The Scientific World Journal,2014,2014.
APA Zeng X.,Wong D.F.,&Chao L.S..(2014).Constructing better classifier ensemble based on weighted accuracy and diversity measure.The Scientific World Journal,2014.
MLA Zeng X.,et al."Constructing better classifier ensemble based on weighted accuracy and diversity measure".The Scientific World Journal 2014(2014).
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