Feature Selection Method Based on Good Point-Set Genetic Algorithm

To address the contradiction between the dimension reduction for feature selection and the precision of classification,by analyzing the strengths and weaknesses of the traditional feature selection method,combines the idea of good point-set genetic algorithm and the simple and effective features of K nearest neighbor classification,presents a new feature selection method based on good point set genetic algorithms.Through a random search of the feature subset with the good point-set genetic algorithm,and using K nearest neighbor classification error rate as the evaluation index,eliminate the bad feature subset,save the optimum feature subset.It can be seen through the comparison experiments that the algorithm can effectively find out those feature subset which has high classification accuracy,and the effect of dimension reduction is good,these show that the algorithm has the better ability to select feature subset.