Research of Poll Mode and Multi-criteria Feature Selection Algorithm Based on Chain-like Agent Genetic Algorithm

Feature selection is a pretreatment process for complex pattern classification systems. According to the low precision of feature selection under filter mode and high time cost of feature selection under wrapper mode, a new feature selection algorithm was proposed. This algorithm designed chain-like agent genetic algorithm as searching algorithm, introduced several evaluation criteria for poll mode selection. The experiments were done to compare this algorithm and several other feature selection algorithms. The experimental results show that this algorithm can obtain better precise selection result than several single evaluation criterion feature selection algorithms under filter mode, and less selection time cost than feature selection algorithm under wrapper mode. Therefore, this algorithm can be used for designing feasible pattern classification system with high recognition rate.