计算机应用研究2017，Vol.34Issue(12)：3713-3716,4.DOI:10.3969/j.issn.1001-3695.2017.12.044
基于最近最远邻和互信息的特征选择方法
Feature selection method based on the nearest & farthest neighbors and mutual information
摘要
Abstract
As to increase the amount of data,feature selection has become a hotspot in the field of machine learning and data mining.This paper proposed a nearest neighbors and farthest neighbors feature selection algorithm(NFFS).The nearest neighboring points of a data point belonged to the same cluster,and the furthest points belonged to a different cluster.Through calculating distances of the nearest cluster and the farthest cluster,it could get an indicator of judging characteristic importance.On the basis,it used the mutual information criterion to get rid of the redundancy between the features.At the same time,it introduced the Gradient boosting method to the tuning parameters of model.This method could improve the classification accuracy.By categorical forecasting on the UCI data sets,the results show that the algorithm can find the optimal feature subset and improve the classification accuracy.关键词
特征选择/最近最远邻/互信息/梯度下降Key words
feature selection/the nearest and farthest neighbors/mutual information/gradient boosting分类
信息技术与安全科学引用本文复制引用
吴雨,刘媛华..基于最近最远邻和互信息的特征选择方法[J].计算机应用研究,2017,34(12):3713-3716,4.基金项目
国家自然科学基金资助项目(11505114) （11505114）
