数据采集与处理2018，Vol.33Issue(2)：317-322,6.DOI:10.16337/j.1004-9037.2018.02.014
基于样本邻域保持的代价敏感特征选择
Cost-Sensitive Feature Selection Based on Sample Neighborhood Preserving
摘要
Abstract
Feature selection is an important preprocessing step in machine learning and data mining.Feature selection of class-imbalanced dataset is a hot topic of machine learning and pattern recognition.Most traditional feature selection classification algorithms pursue high precision,and assume that the data have no misclassification costs or have the same costs.However,in real applications,different misclassifications always tend to produce different misclassification costs.To get the feature subset with minimum misclassification cost,a supervised cost-sensitive feature selection algorithm based on sample neighborhood preserving is proposed,whose main idea is to introduce the sample neighborhood into the cost-sensitive feature selection framework.The experimental results on eight real-life data sets demonstrate the superiority of the proposed algorithm.关键词
特征选择/邻域保持/有监督学习/代价敏感Key words
feature selection/neighborhood preserving/supervised learning/cost-sensitive分类
信息技术与安全科学引用本文复制引用
余胜龙,赵红..基于样本邻域保持的代价敏感特征选择[J].数据采集与处理,2018,33(2):317-322,6.基金项目
国家自然科学基金(61703196)资助项目 （61703196）
福建省教育厅科技项目(JAT160305)资助项目. （JAT160305）
