计算机工程与应用2016，Vol.52Issue(17)：93-100,8.DOI:10.3778/j.issn.1002-8331.1506-0067
融合特征排序的多标记特征选择算法
Multi-label feature selection via fusing feature ranking
摘要
Abstract
In the framework of multi-label learning, feature selection is a powerful tool for solving the curse of dimension-ality, which can improve the classification performance of multi-label classifier. In this paper, a multi-label feature selec-tion algorithm via fusing feature ranking is proposed. First, it conducts adaptive graining samples based on different labels and employs the neighborhood of sample to compute the neighborhood mutual information between feature and label, which can measure the importance degree of feature. Then, all features are sorted in descending order by the value of their neighborhood mutual information under each label. Finally, it acquires a new feature rank by fusing all individual feature rank lists. Experiment is conducted on four data sets, and four evaluation criteria are used to measure the effectiveness. Experi-mental results show that the proposed algorithm is superior to several state-of-the-art multi-label feature selection algorithms.关键词
特征选择/多标记分类/聚类融合/互信息Key words
feature selection/multi-label classification/clustering ensemble/mutual information分类
信息技术与安全科学引用本文复制引用
王晨曦,林梦雷,刘景华,王娟,林耀进..融合特征排序的多标记特征选择算法[J].计算机工程与应用,2016,52(17):93-100,8.基金项目
国家自然科学基金（No.61303131，No.61379021）；福建省自然科学基金（No.2013J01028）；福建省高校杰出青年科研人才培育计划（No.JA14192）；漳州市科技项目（No.ZZ2013J04，No.ZZ2014J14）。 （）
