计算机工程与应用2017，Vol.53Issue(12)：70-75,6.DOI:10.3778/j.issn.1002-8331.1605-0185
最小冗余最大分离准则特征选择方法
Method based on minimum redundancy and maximum separability for feature selec-tion
摘要
Abstract
Feature selection is an effective technique for analyzing high-dimensional data. To improve the performance of traditional feature selection methods, a novel criterion function named minimum redundancy and maximum separability for feature selection is proposed by combining the F-score and mutual information. Based on the new criterion function, the features select own a better ability for classification and prediction. Binary cuckoo search algorithm and quadratic pro-gramming algorithm are adopted to search the optimal subset of features, the accuracy and the amount of computations for feature selection of these two search strategies are analyzed. Finally, the effectiveness of the proposed principle is verified by the experimental results though conducting tests on UCI datasets.关键词
高维数据/费希尔得分/搜索策略/特征选择Key words
high-dimensional data/F-score/search strategy/feature selection分类
信息技术与安全科学引用本文复制引用
赖学方,贺兴时..最小冗余最大分离准则特征选择方法[J].计算机工程与应用,2017,53(12):70-75,6.基金项目
陕西省软科学研究项目(No.2014KRM28-01) （No.2014KRM28-01）
陕西省教育厅专项科研计划项目(No.16JK1341) （No.16JK1341）
西安市2015基础教育研究重大招标项目(No.2015ZB-ZY04) （No.2015ZB-ZY04）
西安工程大学研究生创新基金资助项目(No.CX201614). （No.CX201614）
