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Characterization of classification algorithms

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Progress in Artificial Intelligence (EPIA 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 990))

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Abstract

This paper is concerned with the problem of characterization of classification algorithms. The aim is to determine under what circumstances a particular classification algorithm is applicable. The method used involves generation of different kinds of models. These include regression and rule models, piecewise linear models (model trees) and instance based models. These are generated automatically on the basis of dataset characteristics and given test results. The lack of data is compensated for by various types of preprocessing. The models obtained are characterized by quantifying their predictive capability and the best models are identified.

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Carlos Pinto-Ferreira Nuno J. Mamede

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© 1995 Springer-Verlag Berlin Heidelberg

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Gama, J., Brazdil, P. (1995). Characterization of classification algorithms. In: Pinto-Ferreira, C., Mamede, N.J. (eds) Progress in Artificial Intelligence. EPIA 1995. Lecture Notes in Computer Science, vol 990. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60428-6_16

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  • DOI: https://doi.org/10.1007/3-540-60428-6_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60428-0

  • Online ISBN: 978-3-540-45595-0

  • eBook Packages: Springer Book Archive

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