Exploiting reliability for dynamic selection of classi .ers by means of genetic algorithms

Abstract

We introduce a multiple classifier systemthat incorporates a global optimization technique based ona Genetic Algorithm for dynamically selecting the set ofexperts to use in the majority vote approach. The proposedtechnique is applicable when the experts in the pool provideboth the class assigned to the input sample and a measureof the reliability of the this classification. For each sample,the experts selected for participating in the majority voteare those whose reliability is larger than a given threshold.There are as many thresholds as the number of experts bythe number of classes. The values of the thresholds aimedat selecting the best set of experts for each input sampleare determined by a canonical Genetic Algorithm. Thereliability measures provided by the experts of the pool arealso used to implement the tie-break mechanism neededwithin the majority vote scheme. The system has beentested on a handwritten digit recognition problem, and itsperformance compared with those exhibited by other multi-expertsystems exploiting different combining rules.