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.
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