Error Rejection in Linearly Combined Multiple Classifiers

Authors:
Giorgio Fumera;Fabio Roli
Affiliations:
-;-
Venue:
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Year:
2001

Citing 6
Cited 0

Democracy in neural nets: voting schemes for classification

Neural Networks
Optimal combinations of pattern classifiers

Pattern Recognition Letters
Optimal Linear Combination of Neural Networks for Improving Classification Performance

IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of neural and statistical algorithms for supervised classification of remote-sensing images

Pattern Recognition Letters
Application of majority voting to pattern recognition: an analysis of its behavior and performance

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Improving model accuracy using optimal linear combinations of trained neural networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, the error-reject trade-off of linearly combined multiple classifiers is analysed in the framework of the minimum risk theory. Theoretical analysis described in [12,13] is extended for handling reject option and the optimality of the error-reject trade-off is analysed under the assumption of independence among the errors of the individual classifiers. Improvements of the error-reject trade-off obtained by linear classifier combination are quantified. Finally, a method for computing the coefficients of the linear combination and the value of the reject threshold is proposed. Experimental results on four different data sets are reported.