通信学报2018，Vol.39Issue(5)：111-122,12.DOI:10.11959/j.issn.1000-436x.2018082
基于最大相关最小冗余联合互信息的多标签特征选择算法
Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancy
摘要
Abstract
Feature selection has played an important role in machine learning and artificial intelligence in the past dec-ades. Many existing feature selection algorithm have chosen some redundant and irrelevant features, which is leading to overestimation of some features. Moreover, more features will significantly slow down the speed of machine learning and lead to classification over-fitting. Therefore, a new nonlinear feature selection algorithm based on forward search was proposed. The algorithm used the theory of mutual information and mutual information to find the optimal subset associ-ated with multi-task labels and reduced the computational complexity. Compared with the experimental results of nine datasets and four different classifiers in UCI, the proposed algorithm is superior to the feature set selected by the original feature set and other feature selection algorithms.关键词
特征选择/条件互信息/特征交互/特征相关/特征冗余Key words
feature selection/conditional mutual information/feature interaction/feature relevance/feature redundancy分类
信息技术与安全科学引用本文复制引用
张俐,王枞..基于最大相关最小冗余联合互信息的多标签特征选择算法[J].通信学报,2018,39(5):111-122,12.基金项目
国家科技基础性工作专项基金资助项目(No.2015FY111700-6)The National Science and Technology Basic Work Project(No.2015FY111700-6) （No.2015FY111700-6）
