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Record type:	stať ve sborníku (D)
Home Department:	Ústav pro výzkum a aplikace fuzzy modelování (94410)
Title:	Probabilistic Coverage of Linguistic IF-THEN Rules
Citace	Rusnok, P. Probabilistic Coverage of Linguistic IF-THEN Rules. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016): IEEE International Fuzzy Systems Conference Proceedings 2016-07-24 Vancouver. Vancouver, CANADA: Institute of Electrical and Electronics Engineers Inc., 2016. s. 798-803. ISBN 9781509006250.

Subtitle
Publication year:	2016
Obor:	Obecná matematika
Number of pages:	6
Page from:	798
Page to:	803
Form of publication:	Elektronická verze
ISBN code:	9781509006250
ISSN code:	1098-7584
Proceedings title:	IEEE International Fuzzy Systems Conference Proceedings
Proceedings:	Mezinárodní
Publisher name:	Institute of Electrical and Electronics Engineers Inc.
Place of publishing:	Vancouver, CANADA
Country of Publication:
Název konference:	2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016)
Místo konání konference:	Vancouver
Datum zahájení konference:
Typ akce podle státní příslušnosti účastníků:	Celosvětová akce
WoS code:
EID:

Key words in English:	fuzzy, deduction
Annotation in original language:	Fuzzy associational analysis (FAA) is useful for solving various regression tasks. Mined fuzzy associations can be used as fuzzy IF-THEN rules in a fuzzy expert system, which is then able to predict some variable that appears on the right hand side of IF-THEN rules. In this paper we investigate fuzzy IF-THEN rules reduction methods. We propose a new probabilistic data coverage measure for evaluating quality of a set of IF-THEN rules. We also propose a rule reduction procedure based on this measure and compare it theoretically and experimentally in the context of Perception-based Logical Deduction.
Annotation in english language:	Fuzzy associational analysis (FAA) is useful for solving various regression tasks. Mined fuzzy associations can be used as fuzzy IF-THEN rules in a fuzzy expert system, which is then able to predict some variable that appears on the right hand side of IF-THEN rules. In this paper we investigate fuzzy IF-THEN rules reduction methods. We propose a new probabilistic data coverage measure for evaluating quality of a set of IF-THEN rules. We also propose a rule reduction procedure based on this measure and compare it theoretically and experimentally in the context of Perception-based Logical Deduction.

References

Reference

R01:	RIV/61988987:17610/16:A1701I0E

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