Combination of multiple classifiers using local accuracy estimates

被引：676

作者：

Woods, K ^{[1
]}

Kegelmeyer, WP ^{[1
]}

Bowyer, K ^{[1
]}

机构：

[1] SANDIA NATL LABS, CTR COMPUTAT ENGN, LIVERMORE, CA 94551 USA

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1997年 / 19卷 / 04期

关键词：

combination of classifiers; dynamic classifier selection; local classifier accuracy; classifier fusion; ROC analysis;

D O I：

10.1109/34.588027

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a method for combining classifiers that uses estimates of each individual classifier's local accuracy in small regions of feature space surrounding an unknown test sample. An empirical evaluation using five real data sets confirms the validity of our approach compared to some other Combination of Multiple Classifiers algorithms. We also suggest a methodology for determining the best mix of individual classifiers.

引用

页码：405 / 410

页数：6

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