On the relationship between majority vote accuracy and dependency in multiple classifier systems

Authors:
Sang-Bong Oh
Affiliations:
Division of Computer and Communications Engineering, Daejeon University, 96-3 Youngun-dong, Dong-gu, Daejeon 300-716, South Korea
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

We define two simple probability models of dependency between classifiers: a multiplicative form and an additive form. This paper explores the relationship between the majority vote accuracy and dependency for the three classifiers. We show that the majority votes with negatively dependent classifiers can offer an improvement over independent classifiers and that those with positively dependent classifiers can also offer an improvement over individual classifiers.