[1]FENG Dan,HUANG Yang,SHI Yunpeng,et al.A discernibility matrix-based attribute reduction for continuous data[J].CAAI Transactions on Intelligent Systems,2017,12(3):371-376.[doi:10.11992/tis.201704032]
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A discernibility matrix-based attribute reduction for continuous data

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 3 Page number: 371-376 Column: 学术论文—智能系统 Public date: 2017-06-25
Title:
A discernibility matrix-based attribute reduction for continuous data
Author(s):
FENG Dan1; 2;  HUANG Yang2;  SHI Yunpeng2;  Wang Changzhong2
1. Information and Communication Branch, State Grid Power Supply Company of Huludao, Huludao 125000, China;
2. College of Mathematics and Physics, Bohai University, Jinzhou 121000, China
Keywords:
neighborhood relation; rough set; attribute reduction; discernibility matrix; heuristic algorithm
CLC:
TP391;TP274
DOI:
10.11992/tis.201704032
Abstract:
In data processing, attribute reduction is an important application of rough set theory. The existing methods for continuous data mainly concentrate on the greedy algorithms based on the positive region. These methods take account of only the identifiability between consistent samples and other samples while ignoring distinguishability among the boundary samples. To overcome the disadvantage based on the positive domain algorithm, this paper proposed a new method for attribute reduction using a discernibility matrix. The model considers not only the consistency of samples in the positive region but also the reparability of boundary samples. On this basis, this paper analyzes the structure of attribute reduction and defines a discernibility matrix to characterize the discernibility ability of a subset of attributes. Next, an attribute reduction algorithm was designed based on the discernibility matrix. The validity of the proposed algorithm was verified using UCI standard data sets and theoretical analysis.
References:
[1] PAWLAK Z. Rough sets [J]. International journal of computer and information sciences, 1982, 11(5): 341-356.
[2] SKOWRON A,RAUSZER C. The discernibility matrices and functions in information systems[C]//Slowinski R. (Ed.), Intelligent Decision Support. Dordrecht, Kluwer Academic Publishers, 1992: 331-362.
[3] MI J S, WU W Z, ZHANG W X. Approaches to knowledge reduction based on variable precision rough sets model [J]. Information sciences, 2004, 159(3/4): 255-272.
[4] WU W Z, ZHANG W X. Neighborhood operator systems and approximations [J]. Information sciences, 2002, 144(1/4): 201-217.
[5] HU Q H, YU D, LIU J F, et al. Neighborhood-rough-set based heterogeneous feature subset selection [J]. Information sciences, 2008, 178(18): 3577-3594.
[6] KIM D. Data classification based on tolerant rough set [J]. Pattern recognition, 2001, 34(8): 1613-1624.
[7] ZHAO H, WANG P, HU Q H. Cost-sensitive feature selection based on adaptive neighborhood granularity with multi-level confidence [J]. Information sciences, 2016, 366: 134-149.
[8] CHEN Y, ZHANG Z, ZHENG J, et al. Gene selection for tumor classification using neighborhood rough sets and entropy measures [J]. Journal of biomedical informatics, 2017, 67:59-68
[9] ZHU P, HU Q H. Adaptive neighborhood granularity selection and combination based on margin distribution optimation [J]. Information sciences, 2013, 249:1-12.
[10] 鲍丽娜,丁世飞, 许新征, 等. 基于邻域粗糙集的极速学习机算法[J]. 济南大学学报,2015, 29(5): 367-371.BAO Lina, DING Shifei, XU Xinzheng, et al. Extreme learning machine algorithm based on neighborhood rough sets[J]. Journal of jinan university, 2015, 29(5): 367-371.
[11] 谢娟英, 李楠, 乔子芮. 基于邻域粗糙集的不完整决策系统特征选择算法[J]. 南京大学学报,2016, 47:384-390.XIE Juanying, LI Nan, QIAO Zirui. A feature selection algorithm based on neighborhood rough sets for incomplete information systems[J]. Journal of Nanjing university, 2016, 47:384-390.
[12] 徐伟华. 序信息系统与粗糙集[M]. 北京: 科学出版社, 2013.
[13] GRECO S, MATARAZZO B, SLOWINSKI R. Rough sets methodology for sorting problems in presence of multiple attributes and criteria[J]. European journal of operational research, 2002, 38:247-259.
[14] WANG C, HE Q, CHEN D G, et al. A novel method for attribute reduction of covering decision tables[J]. Information sciences, 2014, 254: 181-196.
[15] WANG C, SHAO M, SUN B, et al. An improved attribute reduction scheme with covering based rough sets[J]. Applied soft computing, 2015, 26(1): 235-243.
[16] ZHU W, WANG F Y. Reduction and maximization of covering generalized rough sets[J]. Information sciences, 2003, 152: 217-230.
[17] DUBOIS D, PRADE H. Rough fuzzy sets and fuzzy rough sets[J]. International journal of general systems, 1990, 17: 191-208.
[18] WANG C, QI Y, HU Q, et al. A fitting model for feature selection with fuzzy rough sets[J]. IEEE transaction on fuzzy systems, 2016,99: 1-1.
[19] WANG C,SHAO M, QIAN Y. Feature subset selection based on fuzzy neighborhood rough sets[J]. Knowledge-based systems, 2016, 111(1): 173-179.
[20] CHEN D G, ZHANG L, ZHAO S Y, et al. A novel algorithm for finding reducts with fuzzy rough sets[J]. IEEE transaction on fuzzy systems, 2013, 20(2): 385-389.
[21] WANG X Z, ZHAI J H, LU S X. Induction of multiple fuzzy decision trees based on rough set technique[J]. Information sciences, 2008, 178(16): 3188-3202.
[22] ZHAO S Y, TSANG C C, CHEN D. Building a rule-based classifier by using fuzzy rough set technique[J]. IEEE transaction on knowledge and data engineering, 2010, 22(5): 624-638.
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