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Typ záznamu:
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Domácí pracoviště:
Ústav pro výzkum a aplikace fuzzy modelování (94410)
Název:
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.
Podnázev
Rok vydání:
2016
Obor:
Obecná matematika
Počet stran:
6
Strana od:
798
Strana do:
803
Forma vydání:
Elektronická verze
Kód ISBN:
9781509006250
Kód ISSN:
1098-7584
Název sborníku:
IEEE International Fuzzy Systems Conference Proceedings
Sborník:
Mezinárodní
Název nakladatele:
Institute of Electrical and Electronics Engineers Inc.
Místo vydání:
Vancouver, CANADA
Stát vydání:
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
Kód UT WoS:
EID:
Klíčová slova anglicky:
fuzzy, deduction
Popis v původním jazyce:
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.
Popis v anglickém jazyce:
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.
Seznam ohlasů
Ohlas
R01:
RIV/61988987:17610/16:A1701I0E
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