智能系统学报2017，Vol.12Issue(3)：397-404,8.DOI:10.11992/tis.201705004
基于粗糙集相对分类信息熵和粒子群优化的特征选择方法
A feature selection approach based on rough set relative classification information entropy and particle swarm optimization
摘要
Abstract
Feature selection, an important step in data mining, is a process that selects a subset from an original feature set based on some criteria.Its purpose is to reduce the computational complexity of the learning algorithm and to improve the performance of data mining by removing irrelevant and redundant features.To deal with the problem of discrete values, a feature selection approach was proposed in this paper.It uses a particle swarm optimization algorithm to search the optimal feature subset.Further, it employs relative classification information entropy as a fitness function to measure the significance of the feature subset.Then, the proposed approach was compared with other evolutionary algorithm-based methods of feature selection.The experimental results confirm that the proposed approach outperforms genetic algorithm-based methods.关键词
数据挖掘/特征选择/数据预处理/粗糙集/决策表/粒子群算法/信息熵/适应度函数Key words
data mining/feature selection/data preprocessing/rough set/decision table/particle swarm optimization/information entropy/fitness function分类
信息技术与安全科学引用本文复制引用
翟俊海,刘博,张素芳..基于粗糙集相对分类信息熵和粒子群优化的特征选择方法[J].智能系统学报,2017,12(3):397-404,8.基金项目
国家自然科学基金项目(71371063) （71371063）
河北省自然科学基金项目(F2017201026) （F2017201026）
浙江省计算机科学与技术重中之重学科(浙江师范大学)资助项目. （浙江师范大学）
