Designing combining classifier with trained fuser — Analytical and experimental evaluation

Combining pattern recognition is the promising direction in designing an effective classifier systems. There are several approaches of collective decision-making, among them voting methods, where the decision is a combination of individual classifiers´ outputs are quite popular. This article focuses on the problem of fuser design which uses continuous outputs of individual classifiers to make a decision. We formulate problem of fuser design as an optimization task and use neural approach as its solver. We propose a taxonomy of aforementioned fusers and their main features are presented for some of them. The results of computer experiments carried out on benchmark datasets confirm quality of proposed concept.