[1]高琪,李德玉,王素格.基于模糊不一致对的多标记属性约简[J].智能系统学报,2020,15(2):374-385.[doi:10.11992/tis.201905046]
 GAO Qi,LI Deyu,WANG Suge.Multi-label attribute reduction based on fuzzy inconsistency pairs[J].CAAI Transactions on Intelligent Systems,2020,15(2):374-385.[doi:10.11992/tis.201905046]
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基于模糊不一致对的多标记属性约简(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年2期
页码:
374-385
栏目:
人工智能院长论坛
出版日期:
2020-07-09

文章信息/Info

Title:
Multi-label attribute reduction based on fuzzy inconsistency pairs
作者:
高琪1 李德玉12 王素格12
1. 山西大学 计算机科学与信息技术学院, 山西 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, 山西 太原 030006
Author(s):
GAO Qi1 LI Deyu12 WANG Suge12
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;
2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education University, Shanxi University, Taiyuan 030006, China
关键词:
多标记数据属性约简模糊不一致对标记权重KL散度标记关系模糊粗糙集区分矩阵
Keywords:
multi-label dataattribute reductionfuzzy inconsistency pairslabel weightKullback-Leibler divergencethe relationship of labelsfuzzy rough setsdistinguished matrix
分类号:
TP391
DOI:
10.11992/tis.201905046
摘要:
在实际生活当中,存在着大量的高维多标记数据,为解决维度灾难问题,通常需要约简属性集。针对目前的多标记属性约简算法未考虑标记关系问题,本文提出了一种融合标记关系的模糊不一致对多标记属性约简算法。利用相对熵(KL散度)度量标记之间的关系,定义标记权重,结合标记权重,定义模糊不一致对,考虑到属性对于模糊不一致对的区分性,定义属性重要性并进行属性约简。在8个数据集上的对比实验表明,所提基于模糊不一致对的多标记属性约简算法优于当前的多标记属性约简算法。
Abstract:
In real life, there is a large amount of multi-label data, and in multi-label data processing, attribute reduction is one of the important methods to solve the high-dimensional disaster of multi-label data. Because there is a relationship between labels, in this paper we firstly use the KL divergence metric to determine the relationship between labels, then define the label weight, and then combine the label weight to define the fuzzy inconsistency pairs. Finally, considering the distingishing ability of attributes to the fuzzy inconsistency pairs, we propose a multi-label attribute reduction algorithm based on fuzzy inconsistency pairs. Extensive experiments carried out on eight publicly available data sets verify effectiveness of the proposed algorithm named MLAR-FL by comparing it with some state-of-the-art approaches.

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备注/Memo

备注/Memo:
收稿日期:2019-05-24。
基金项目:国家自然科学基金项目(61672331, 61573231, 61432011, 61802237);山西省重点研发计划项目 (201803D421024, 201903D421041);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
作者简介:高琪,硕士研究生,主要研究方向为粗糙集、多标记学习;李德玉,教授,博士,主要研究方向为粒计算、机器学习,多标记学习。主持国家自然科学基金项目2项,参加过3项国家863计划项目等。出版著作2部,发表学术论文80余篇;王素格,教授,博士,主要研究方向为自然语言处理、文本挖掘。主持国家自然科学基金项2项,山西省自然科学基金1项。发表学术论文80余篇
通讯作者:李德玉.E-mail:lidy@sxu.edu.cn
更新日期/Last Update: 1900-01-01