Fuzzy rough sets and multiple-premise gradual decision rules

被引:104
作者
Greco, S
Inuiguchi, M
Slowinski, R
机构
[1] Univ Catania, Fac Econ, I-95129 Catania, Italy
[2] Osaka Univ, Grad Sch Engn Sci, Osaka 5608531, Japan
[3] Poznan Univ Technol, Inst Comp Sci, PL-60965 Poznan, Poland
[4] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland
关键词
rough sets; fuzzy sets; decision rides; gradual rules; credibility;
D O I
10.1016/j.ijar.2005.06.014
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose a new fuzzy rough set approach which.. differently from most known fuzzy set extensions of rough set theory, does not use any fuzzy logical connectives (t-norm, t-conorm, fuzzy implication). As there is no rationale for a particular choice of these connectives, avoiding this choice permits to reduce the part of arbitrary in the fuzzy rough approximation. Another advantage of the new approach is that it is based on the ordinal properties of fuzzy membership degrees only. The concepts of fuzzy lower and Upper approximations are thus proposed, creating a base for induction or fuzzy decision rules having syntax and semantics of gradual rules. The proposed approach to rule induction is also interesting from the viewpoint Of philosophy supporting data mining and knowledge discovery, because it is concordant with the method of concomitant variations by John Stuart Mill. The decision rules are induced from lower and upper approximations defined for positive and negative relationships between credibility degrees of multiple premises, on one hand, and conclusion, on the other hand. (C) 2005 Elsevier Inc. All rights reserved.
引用
收藏
页码:179 / 211
页数:33
相关论文
共 21 条
[1]  
[Anonymous], 1951, Tractatus logico-philosophicus, V12, P1
[2]  
[Anonymous], 1991, ROUGH SETS
[3]  
[Anonymous], ADV MULTIPLE CRITERI
[4]  
ARAGONES E, 2002, 02027 PIER
[5]  
BENSUSAN H, 2000, ECAI2000 WORKSH NOT, P9
[6]  
CORNISH TAO, 1995, PR CONF ART INT APPL, P347, DOI 10.1109/CAIA.1995.378801
[7]   GRADUAL INFERENCE RULES IN APPROXIMATE REASONING [J].
DUBOIS, D ;
PRADE, H .
INFORMATION SCIENCES, 1992, 61 (1-2) :103-122
[8]  
Dubois D., 1992, INTELLIGENT DECISION, P203, DOI 10.1007/978-94-015-7975-9_14
[9]  
DUBOIS D, 1997, INFORM ENG
[10]   Can Bayesian confirmation measures be useful for rough set decision rules? [J].
Greco, S ;
Pawlak, Z ;
Slowinski, R .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2004, 17 (04) :345-361