Genetic algorithm-based neural error correcting output classifier

Abstract

The present study elaborates a probabilistic framework of ECOC technique, via replacement of predesigned ECOC matrix by sufficiently large random codes. Further mathematical grounds of deploying random codes through probability formulations are part of novelty of this study. Random variants of ECOC techniques were applied in previous literatures, however, often failing to deliver sufficient theoretical proof of efficiency of random coding matrix. In this paper a Genetic Algorithm-based neural encoder with redefined operators is designed and trained. A variant of heuristic trimming of ECOC codewords is also deployed to acquire more satisfactory results. The efficacy of proposed approach was validated over a wide set of datasets of UCI Machine Learning Repository and compared against two conventional methods.