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| 基于双空间模糊辨识关系的多标记特征选择 | |
| 引用本文： | 姚二亮,李德玉,李艳红,白鹤翔,张超.基于双空间模糊辨识关系的多标记特征选择[J].模式识别与人工智能,2019,32(8):709-717. |
| 作者姓名： | 姚二亮 李德玉 李艳红 白鹤翔 张超 |
| 作者单位： | 1.山西大学 计算机与信息技术学院 太原 030006 2.山西大学 计算智能与中文信息处理教育部重点实验室 太原 030006 |
| 基金项目： | 国家自然科学基金项目(No.61672331,61573231,61432011,61806116)、山西省重点研发计划项目(No.201803D42102)、山西省自然科学基金项目(No.201701D121055,201801D221175)、山西省高等学校科技创新项目(No.201802014)、山西省高等学校优秀成果培育项目(2019SK036)、山西省高等学校青年科研人员培育计划资助 |
| 摘 要： | 已有的基于模糊粗糙集的多标记特征选择算法多从单一的样本空间刻画属性区分能力,忽视属性对标记的区分能力.基于这一认识,文中同时从样本和标记两个空间出发,提出基于双空间模糊辨识关系的多标记特征选择算法.首先,基于模糊辨识关系分别从样本和标记角度定义两种多标记属性重要性度量,然后通过权重融合的方式融合两种度量,基于融合后的度量,运用前向贪心算法构建多标记特征选择算法.在5个数据集上的对比实验验证本文算法的有效性. |
| 关 键 词： | 多标记学习 特征选择 模糊粗糙集 模糊辨识关系 |
| 收稿时间： | 2019-03-15 |
Multi-label Feature Selection Based on Fuzzy Discernibility Relations in Double Spaces |
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| YAO Erliang,LI Deyu,LI Yanhong,BAI Hexiang,ZHANG Chao.Multi-label Feature Selection Based on Fuzzy Discernibility Relations in Double Spaces[J].Pattern Recognition and Artificial Intelligence,2019,32(8):709-717. | |
| Authors: | YAO Erliang LI Deyu LI Yanhong BAI Hexiang ZHANG Chao |
| Affiliation: | 1.School of Computer and Information Technology, Shanxi University, Taiyuan 030006 2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006 |
| Abstract: | The existing multi-label feature selection algorithms based on fuzzy rough sets characterize the ability of distinguishing attributes from single sample space, while the ability of attributes distinguishing labels is ignored. Therefore, a multi-label feature selection algorithm based on fuzzy discernibility relations in double spaces is proposed. Firstly, two multi-label attribute measures based on fuzzy discernibility relations are defined from the perspective of samples and labels respectively. Then, two different measures are combined by introducing weights. Finally, a multi-label feature selection algorithm is constructed based on the combined measures by utilizing the forward greedy algorithm. Results of comparative experiments on five multi-label datasets verify the effectiveness of the proposed algorithm. |
| Keywords: | Multi-label Learning Feature Selection Fuzzy Rough Sets Fuzzy Discernibility Relation |
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