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Dep.Telecommunications and Information Processing
BibTeX
@MISC{Kordjamshidi_dep.telecommunicationsand,
author = {Parisa Kordjamshidi and Ghent University and Bernard De Baets and Guy De Tre},
title = {Dep.Telecommunications and Information Processing},
year = {}
}
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Abstract
A fuzzy prototype-based method is introduced for learning from exam-ples. A special kind of prototype vector with fuzzy attributes is de-rived for each class from aggregat-ing fuzzified cases for the purpose of concept description. The fuzzi-fied cases are derived by defining a fuzzy membership function for each attribute of the sample cases. In a first method, for the classification of a new case, the membership degrees of its crisp attributes to fuzzy ag-gregated prototypes are measured. In a second method, after fuzzify-ing the new case, fuzzy set compar-ison methods are applied for mea-suring the similarity. The methods are compared to case-based ones like POSSIBL and kNN using UCI ma-chine learning repository. We also make comparisons by using various transformation methods from prob-abilities to possibilities instead of defining membership functions.
Keyphrases
information processing new case first method second method prototype vector fuzzy membership function fuzzy prototype-based method compar-ison method various transformation method concept description ag-gregated prototype membership function uci ma-chine learning repository aggregat-ing fuzzified case special kind case-based one membership degree fuzzy attribute fuzzi-fied case sample case
