Vote counting measures for ensemble classifiers

被引：22

作者：

Windeatt, T ^{[1
]}

机构：

[1] Univ Surrey, Sch Elect Engn, Ctr Vis Speech & Signal Proc, Guildford GU2 7XH, Surrey, England

来源：

PATTERN RECOGNITION | 2003年 / 36卷 / 12期

关键词：

decision level fusion; multiple classifiers; ensembles; error-correcting; binary coding;

D O I：

10.1016/S0031-3203(03)00191-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Various measures, such as Margin and Bias/Variance, have been proposed with the aim of gaining a better understanding of why Multiple Classifier Systems (MCS) perform as well as they do. While these measures provide different perspectives for MCS analysis, it is not clear how to use them for MCS design. In this paper a different measure based on a spectral representation is proposed for two-class problems. It incorporates terms representing positive and negative correlation of pairs of training patterns with respect to class labels. Experiments employing MLP base classifiers, in which parameters are fixed but systematically varied, demonstrate the sensitivity of the proposed measure to base classifier complexity. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：2743 / 2756

页数：14

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