Error Rejection in Linearly Combined Multiple Classifiers

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.

• Type: Conference
• Year: 2001
• Where: MCS
• Authors: Giorgio Fumera, Fabio Roli
• Keywords: Error-reject Trade-off, Linear Classifier Combination, MCS 2001, Minimum Risk Theory, Pattern Recognition