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Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A03103447" target="_blank" >RIV/68407700:21230/04:03103447 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Automatic generation of fuzzy rules and its applications in medical diagnosis

Popis výsledku v původním jazyce

Fuzzy Rule Learner (FURL) is a theory revision approach to fuzzy rules learning based on Hierarchical Prioritized Structures. Each new level is composed from exceptions to rules from the preceding levels. The new rules are chosen in order to eliminate the biggest classification errors found in the training data. FURL may me combined with many techniques used to interpret rule bases in fuzzy controllers. In the traditional approaches to fuzzy approximation, the learning of rules has an undesirable effect. When many new rules are added, the interpretation of the rule base tends to one of its extreme values, thus we loose its informational value. In this paper, we suggest and test two methods which may overcome this drawback, negated antecedents and a controller with conditionally firing rules. We show that they allow to improve the performance of systems based on learning of fuzzy rules, namely the Fuzzy Rule Learner. The methods are tested on ECG and Multiple Sclerosis Disease datasets.

Název v anglickém jazyce

Automatic generation of fuzzy rules and its applications in medical diagnosis

Popis výsledku anglicky

Fuzzy Rule Learner (FURL) is a theory revision approach to fuzzy rules learning based on Hierarchical Prioritized Structures. Each new level is composed from exceptions to rules from the preceding levels. The new rules are chosen in order to eliminate the biggest classification errors found in the training data. FURL may me combined with many techniques used to interpret rule bases in fuzzy controllers. In the traditional approaches to fuzzy approximation, the learning of rules has an undesirable effect. When many new rules are added, the interpretation of the rule base tends to one of its extreme values, thus we loose its informational value. In this paper, we suggest and test two methods which may overcome this drawback, negated antecedents and a controller with conditionally firing rules. We show that they allow to improve the performance of systems based on learning of fuzzy rules, namely the Fuzzy Rule Learner. The methods are tested on ECG and Multiple Sclerosis Disease datasets.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F02%2F1540" target="_blank" >GA201/02/1540: Vícehodnotové logiky pro soft computing</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2004

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 10th International Conference on Information Processing and Management of Uncertainty

ISBN

88-87242-54-2

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

657-663

Název nakladatele

Universita La Sapienza

Místo vydání

Rome

Místo konání akce

Rome

Datum konání akce

4. 7. 2004

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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