智能系统学报Issue(3)：292-297,6.DOI:10.3969/j.issn.1673-4785.201403064
一种多标记数据的过滤式特征选择框架
A filtering framework fro the multi-label feature selection
摘要
Abstract
The researchers of multi-label learning mainly focus on the classifier performance , regardless of the influ-ence of the dataset feature .This paper proposes a filter framework of the multi-labeled data feature selection .The al-gorithm implementation and experiment were carried out based on the Chi-square test .This framework calculates the CHI-square test for each feature on each label , and then the ranking order of each feature is computed by the statis-tics of the score.This paper considers three different types of statistical data (average, maximum, minimum) for the experimental comparisons .The contrasting experiments with the four common multi-label datasets with three classifiers and five evaluation criteria show that these three score statistical methods share both superior and inferior characteristics, but still improve the performance for multi-label learning problems.关键词
特征选择/多标记/过滤式/卡方检验Key words
feature selection/multi-label/filter/CHI-square test分类
信息技术与安全科学引用本文复制引用
郭雨萌,李国正..一种多标记数据的过滤式特征选择框架[J].智能系统学报,2014,(3):292-297,6.基金项目
国家自然科学基金资助项目（61273305）． （）
