An Evolutionary Approach for Dynamic Configuration of Multi-expert Classification Systems

We introduce a multiple classifier system that incorporates a global optimization technique based on a Breeder Genetic Algorithm for dynamically selecting the set of experts to be included in the pool. The proposed technique is applicable when the experts provide both the class assigned to the input sample and a measure of the reliability of the classification. For each sample, the experts selected for participating in the voting rule are those whose reliability is larger than a given threshold. There are as many thresholds as the number of experts by the number of classes. The values of the thresholds aimed at selecting the best set of experts for each input sample are determined by the Breeder Genetic Algorithm. The reliability measures provided by the experts of the pool are also used to implement the tie-break mechanism needed within the voting scheme. The system has been tested on the Image database from the UCI database repository by using as classifiers an ensemble of Back-Propagation neural network and an ensemble of Learning Vector Quantization neural network. The voting schemes adopted are the Majority Vote, the Weighted Majority Vote and the Borda Count. The performance of the system is compared with those exhibited by the multi-expert systems exploiting the same combining rules without the dynamic selection of the expert.