Exploiting Reliability for Dynamic Selection of Classifiers by Means of Genetic Algorithms

We introduce a multiple classifier system that incorporates a global optimization technique based on a Genetic Algorithm for dynamically selecting the set of experts to use in the majority vote approach. The proposed technique is applicable when the experts in the pool provide both the class assigned to the input sample and a measure of the reliability of the this classification. For each sample, the experts selected for participating in the majority vote 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 a canonical Genetic Algorithm. The reliability measures provided by the experts of the pool are also used to implement the tie-break mechanism needed within the majority vote scheme. The system has been tested on a handwritten digit recognition problem, and its performance compared with those exhibited by other multiexpert systems exploiting different combining rules.