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Predictor output sensitivity and feature similarity-based feature selection
Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID-id: 0000-0003-2185-8973
Department of Applied Electronics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.
Department of Applied Electronics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.
Department of Applied Electronics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.
2008 (Engelska)Ingår i: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 159, nr 4, s. 422-434Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

This paper is concerned with a feature selection technique capable of generating an efficient feature set in a few selection steps. The feature saliency measure proposed is based on two factors, namely, the fuzzy derivative of the predictor output with respect to the feature and the similarity between the feature being considered and the feature set. The use of the fuzzy derivative enables modelling the vagueness that occurs in estimating the predictor output sensitivity. The feature similarity measure employed allows avoiding utilization of very redundant features. The experimental investigations performed on five real world problems have shown the effectiveness of the feature selection technique proposed. The technique developed removed a large number of features from the original data sets without reducing the classification accuracy of a classifier. In contrast, the accuracy of the classifiers utilizing the reduced feature sets was higher than those exploiting all the original features.

Ort, förlag, år, upplaga, sidor
Elsevier, 2008. Vol. 159, nr 4, s. 422-434
Nyckelord [en]
Classification, Design, Measurement, Performance
Nationell ämneskategori
Teknik och teknologier
Identifikatorer
URN: urn:nbn:se:hh:diva-1324DOI: 10.1016/j.fss.2007.05.020ISI: 000253476300004Scopus ID: 2-s2.0-37349087901Lokalt ID: 2082/1703OAI: oai:DiVA.org:hh-1324DiVA, id: diva2:238542
Tillgänglig från: 2008-04-16 Skapad: 2008-04-16 Senast uppdaterad: 2017-12-13Bibliografiskt granskad

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Verikas, Antanas

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