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A fuzzy measure of similarity for instance-based learning

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Book cover Foundations of Intelligent Systems (ISMIS 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1609))

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Abstract

Instance-based learning techniques are based on computing distances or similarities between instances with known classification and objects to classify. The nearest instance or instances are used to predict the class of unseen objects. In this paper we present a fuzzy measure of similarity between fuzzy sets and between elements. This measure allows us to obtain a normalized value of the proximity of objects defined by fuzzy features.

In order to test the efficiency of the proposed measure, we use it in a simple instance-based learning system and make a comparison with other measures proposed in literature.

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Authors and Affiliations

  1. Departamento de Matemática Aplicada, Universidad de Vigo, Campus A Xunqueira, 36005, Pontevedra, Spain

    Francisco Botana

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  1. Francisco Botana
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Editor information

Zbigniew W. Raś Andrzej Skowron

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© 1999 Springer-Verlag Berlin Heidelberg

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Botana, F. (1999). A fuzzy measure of similarity for instance-based learning. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095131

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  • DOI: https://doi.org/10.1007/BFb0095131

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65965-5

  • Online ISBN: 978-3-540-48828-6

  • eBook Packages: Springer Book Archive

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