DocumentCode :
2908657
Title :
Generating fuzzy rules to identify relevant cases in case-based reasoning
Author :
Xiong, Ning
Author_Institution :
Comput. Sci. & Electron. Dept., Malardalen Univ., Vasteras
fYear :
2008
fDate :
1-6 June 2008
Firstpage :
2359
Lastpage :
2364
Abstract :
This paper proposes a new fuzzy case-based reasoning system in which fuzzy rule-based reasoning is utilized as a mechanism for matching between cases. The motivation is that fuzzy if-then rules present a more powerful and flexible means to represent the knowledge about case relevance than traditional distance based similarity measurements. With such fuzzy rules available, every case in the case base can be examined via fuzzy reasoning to predict whether it is relevant to a target problem in query. Those cases that are predicted as relevant are then retrieved and delivered to the next stage of decision fusion. Further, we claim that the set of fuzzy rules for case relevance prediction can be learned from the case base. The key to this is doing pair-wise comparisons of cases with known solutions in the case base such that sufficient samples of case relevance can be derived for fuzzy rule learning. The evaluations conducted on a benchmark data set have shown that the fuzzy rules in demand can be learned from a rather small case base without the risk of over-fitting and that the proposed system yields high information recall rate by capturing more cases that are relevant while not undermining the precision for the set of retrieved cases.
Keywords :
case-based reasoning; fuzzy reasoning; knowledge based systems; case relevance prediction; case-based reasoning; decision fusion; fuzzy rule learning; fuzzy rule-based reasoning; fuzzy rules generation; relevant cases identification; Fuzzy reasoning; Fuzzy systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on
Conference_Location :
Hong Kong
ISSN :
1098-7584
Print_ISBN :
978-1-4244-1818-3
Electronic_ISBN :
1098-7584
Type :
conf
DOI :
10.1109/FUZZY.2008.4630698
Filename :
4630698
Link To Document :
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