A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems

A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems (2005)

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by Giorgio Fumera , Fabio Roli

Venue:	IEEE Transactions on Pattern Analysis and Machine Intelligence
Citations:	15 - 2 self

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Datum	Value	Source
TITLE	A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems	INFERENCE
AUTHOR NAME	Giorgio Fumera	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Electrical and Electronic Engineering, University of Cagliari	SVM HeaderParse 0.2
AUTHOR ADDR	Piazza d’Armi, 09123 Cagliari, Italy	SVM HeaderParse 0.2
AUTHOR NAME	Fabio Roli	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Electrical and Electronic Engineering, University of Cagliari	SVM HeaderParse 0.2
AUTHOR ADDR	Piazza d’Armi, 09123 Cagliari, Italy	SVM HeaderParse 0.2
ABSTRACT	In this paper, a theoretical and experimental analysis of linear combiners for multiple classifier systems is presented. Although linear combiners are the most frequently used combining rules, many important issues related to their operation for pattern classification tasks lack a theoretical basis. After a critical review of the framework developed in works by Tumer and Ghosh, on which our analysis is based, we focus on the simplest and most widely used implementation of linear combiners, which consists in assigning a non-negative weight to each individual classifier. Moreover, we consider the ideal performance of this combining rule, i.e., that achievable when the optimal values of the weights are used. We do not consider the problem of weights estimation, which has been extensively addressed in the literature. Our theoretical analysis shows how the performance of linear combiners, in terms of misclassification probability, depends on the performance of individual classifiers, and on the correlation between their outputs. In particular, we evaluate the ideal performance improvement that can be achieved using the weighted average over the simple average combining rule, and investigate in what way it depends on the individual classifiers. Experimental results on real data sets show that the behaviour of linear combiners agrees with the predictions of our analytical model. Finally, we discuss the contribution to the state of the art and the practical relevance of our theoretical and experimental analysis of linear combiners for multiple classifier systems.	SVM HeaderParse 0.2
YEAR	2005	INFERENCE
VENUE	IEEE Transactions on Pattern Analysis and Machine Intelligence	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	942--956	INFERENCE
VOLUME	27	INFERENCE
CITATIONS	31 found	ParsCit 1.0