Attribute reduction in an incomplete categorical decision information system based on fuzzy rough sets

被引:14
作者
He, Jiali [1 ]
Qu, Liangdong [2 ]
Wang, Zhihong [3 ]
Chen, Yiying [4 ]
Luo, Damei [5 ]
Ching-Feng Wen [6 ,7 ,8 ]
机构
[1] Yulin Normal Univ, Dept Guangxi Educ, Key Lab Complex Syst Optimizat & Big Data Proc, Yulin 537000, Guangxi, Peoples R China
[2] Guangxi Univ Nationalities, Sch Artificial Intelligence, Nanning 530006, Guangxi, Peoples R China
[3] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Inst Artificial Intelligence, Chengdu 611756, Sichuan, Peoples R China
[4] Minnan Normal Univ, Sch Math & Stat, Zhangzhou 363000, Fujian, Peoples R China
[5] Guangxi Univ, Sch Math & Informat Sci, Nanning 530004, Guangxi, Peoples R China
[6] Kaohsiung Med Univ, Ctr Fundamental Sci, Kaohsiung 80708, Taiwan
[7] Kaohsiung Med Univ, Res Ctr Nonlinear Anal & Optimizat, Kaohsiung 80708, Taiwan
[8] Kaohsiung Med Univ Hosp, Dept Med Res, Kaohsiung 80708, Taiwan
基金
中国国家自然科学基金;
关键词
Attribute reduction; ICDIS; Fuzzy rough set; UNCERTAINTY;
D O I
10.1007/s10462-021-10117-w
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Categorical data is an important class of data in machine learning. Information system based on categorical data is called a categorical information system (CIS), a CIS with missing values is known as an incomplete categorical information system (ICIS) and an ICIS with decision attributes is said to be an incomplete categorical decision information system (ICDIS). Attribute selection is an important subject in rough set theory. This paper investigates attribute reduction in an ICDIS based on fuzzy rough sets. To depict the similarity for incomplete categorical data, fuzzy symmetry relations in an ICDIS are first introduced. Then, some attribute-evaluation functions, such fuzzy positive regions, dependency function and attribute importance functions are given. Next, the fuzzy-rough iterative computation model for an ICDIS is presented, and an attribute reduction algorithm in an ICDIS based on fuzzy rough sets is given. Finally, experiments are carried out as so to evaluate the performance of the proposed algorithm, and Friedman test and Bonferroni-Dunn test in statistics are conducted. The experimental results indicate that the proposed algorithm is more effective than some existing algorithms.
引用
收藏
页码:5313 / 5348
页数:36
相关论文
共 45 条
[1]  
[Anonymous], 1991, Theoretical Aspects of Reasoning about Data
[2]   On the connection of hypergraph theory with formal concept analysis and rough set theory [J].
Cattaneo, Gianpiero ;
Chiaselotti, Giampiero ;
Ciucci, Davide ;
Gentile, Tommaso .
INFORMATION SCIENCES, 2016, 330 :342-357
[3]   A Novel Algorithm for Finding Reducts With Fuzzy Rough Sets [J].
Chen, Degang ;
Zhang, Lei ;
Zhao, Suyun ;
Hu, Qinghua ;
Zhu, Pengfei .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2012, 20 (02) :385-389
[4]   Fuzzy Kernel Alignment With Application to Attribute Reduction of Heterogeneous Data [J].
Chen, Linlin ;
Chen, Degang ;
Wang, Hui .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2019, 27 (07) :1469-1478
[5]  
Chen SL., 2005, FUZZY SETS THEORY IT
[6]   Measures of uncertainty for neighborhood rough sets [J].
Chen, Yumin ;
Xue, Yu ;
Ma, Ying ;
Xu, Feifei .
KNOWLEDGE-BASED SYSTEMS, 2017, 120 :226-235
[7]   Attribute selection with fuzzy decision reducts [J].
Cornelis, Chris ;
Jensen, Richard ;
Hurtado, German ;
Slezak, Dominik .
INFORMATION SCIENCES, 2010, 180 (02) :209-224
[8]   Maximal-Discernibility-Pair-Based Approach to Attribute Reduction in Fuzzy Rough Sets [J].
Dai, Jianhua ;
Hu, Hu ;
Wu, Wei-Zhi ;
Qian, Yuhua ;
Huang, Debiao .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 26 (04) :2174-2187
[9]   Attribute selection based on a new conditional entropy for incomplete decision systems [J].
Dai, Jianhua ;
Wang, Wentao ;
Tian, Haowei ;
Liu, Liang .
KNOWLEDGE-BASED SYSTEMS, 2013, 39 :207-213
[10]   ROUGH FUZZY-SETS AND FUZZY ROUGH SETS [J].
DUBOIS, D ;
PRADE, H .
INTERNATIONAL JOURNAL OF GENERAL SYSTEMS, 1990, 17 (2-3) :191-209