An empirical study on diversity measures and margin theory for ensembles of classifiers

Author

Kapp, Marcelo N. ; Sabourin, Robert ; Maupin, Patrick

Author_Institution

Ecole de Technol. Super., Montreal

fYear

2007

fDate

9-12 July 2007

Firstpage

1

Lastpage

8

Abstract

The main goal of this paper is to investigate the relationship between two theories widely applied to explain the success of classifiers fusion: diversity measures and margin theory. In order to achieve this, we realized an empirical study which evaluates some classical measures related to these two theories with respect to ensembles accuracy. In particular, this study revealed valuable insights on how these two theories can influence each other, and how the application of margin based measures can be useful for the evaluation and selection of ensembles of classifiers with majority voting.

Keywords

pattern classification; Margin theory; classifiers fusion; diversity measures; ensemble of classifiers; majority voting; Boosting; Current measurement; Particle measurements; Protocols; Research and development; Support vector machine classification; Support vector machines; Voting; Classifiers Fusion; Diversity Measures; Ensemble of Classifiers; Majority Voting; Margin Theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2007 10th International Conference on

Conference_Location

Quebec, Que.

Print_ISBN

978-0-662-45804-3

Electronic_ISBN

978-0-662-45804-3

Type

conf

DOI

10.1109/ICIF.2007.4408144

Filename

4408144