计算机技术与发展2011，Vol.21Issue(1)：50-52,57,4.
基于佳点集遗传算法的特征选择方法
Feature Selection Method Based on Good Point-Set Genetic Algorithm
摘要
Abstract
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 classifieation,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 algofithm has the better ability to select feature subset.关键词
K最近邻算法/特征选择/佳点集遗传算法分类
信息技术与安全科学引用本文复制引用
贾瑞玉,宁再早,耿锦威,查丰..基于佳点集遗传算法的特征选择方法[J].计算机技术与发展,2011,21(1):50-52,57,4.基金项目
安徽省高等学校省级自然科学基金(KJ2008B002) （KJ2008B002）
