A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm

被引:70
作者
Luan, Xin-Yuan [1 ]
Li, Zhan-Pei [1 ]
Liu, Ting-Zhang [1 ]
机构
[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai 200072, Peoples R China
基金
中国国家自然科学基金;
关键词
Attribute reduction; Rough set; AFSA; Cauchy distribution; NEIGHBORHOOD; KNOWLEDGE;
D O I
10.1016/j.neucom.2015.06.090
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Attribute Reduction (AR) is an important preprocessing step for data mining. AR based on rough set is an efficient method. Its reduction performance has been verified to be better or comparable with other methods in large amount of works, but existing reduction algorithms have some problems such as slow convergent speed and probably converging to a local optimum. A novel attribute reduction algorithm based on Artificial Fish Swarm Algorithm (AFSA) and rough set is proposed. For AFSA has a slow convergence rate in the later phase of iterations, normal distribution function, Cauchy distribution function, multi-parent crossover operator, mutation operator and modified minimal generation gap model are adopted to improve AFSA. The attribute reduction algorithm based on improved AFSA and rough set takes full advantages of the improved AFSA and rough set,which are faster, more efficient, simpler, and easier to be implemented. Datasets in the UC Irvine (UCI) Machine Learning Repository are selected to verify the aforementioned new method. The results show that above algorithm can search the attribute reduction set effectively, and it has low time complexity and the excellent global search ability. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:522 / 529
页数:8
相关论文
共 39 条
[21]   Gene selection using rough set based on neighborhood for the analysis of plant stress response [J].
Meng, Jun ;
Zhang, Jing ;
Li, Rui ;
Luan, Yushi .
APPLIED SOFT COMPUTING, 2014, 25 :51-63
[22]  
Nguyen S.H., 1996, Proceedings of the Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU'96, P1451
[23]   Rough set based image denoising for brain MR images [J].
Phophalia, Ashish ;
Rajwade, Ajit ;
Mitra, Suman K. .
SIGNAL PROCESSING, 2014, 103 :24-35
[24]   A MODEST PROPOSAL - A NEW STANDARD FOR THE NORMAL [J].
STIGLER, SM .
AMERICAN STATISTICIAN, 1982, 36 (02) :137-138
[25]   An improved attribute reduction scheme with covering based rough sets [J].
Wang, Changzhong ;
Shao, Mingwen ;
Sun, Baiqing ;
Hu, Qinghua .
APPLIED SOFT COMPUTING, 2015, 26 :235-243
[26]   A classification approach for less popular webpages based on latent semantic analysis and rough set model [J].
Wang, Jun ;
Peng, Jiaxu ;
Liu, Ou .
EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (01) :642-648
[27]   A clonal real-coded quantum-inspired evolutionary algorithm with Cauchy mutation for short-term hydrothermal generation scheduling [J].
Wang, Yongqiang ;
Zhou, Jianzhong ;
Mo, Li ;
Ouyang, Shuo ;
Zhang, Yongchuan .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2012, 43 (01) :1228-1240
[28]  
Wroblewski J., 1995, Proceedings of the International Workshop on Rough Sets Soft Computing at Second Annual Joint Conference on Information Sciences, P186
[29]   Novel algorithms of attribute reduction with variable precision rough set model [J].
Yang, Yanyan ;
Chen, Degang ;
Dong, Ze .
NEUROCOMPUTING, 2014, 139 :336-344
[30]   A novel variable precision (θ, σ)-fuzzy rough set model based on fuzzy granules [J].
Yao, Yanqing ;
Mi, Jusheng ;
Li, Zhoujun .
FUZZY SETS AND SYSTEMS, 2014, 236 :58-72