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An empirical study on the incompetence of attribute selection criteria

  • Communications Session 5B Learning and Discovery Systems
  • Conference paper
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Foundations of Intelligent Systems (ISMIS 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1079))

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Abstract

One of the main tasks in most supervised learning systems is the evaluation of the attributional relevancy in the given databases. Such relevancy is mainly concerned with the relationship between the available attributes and the decision classes. Attributes relevant to the decision classes are used to represent the learned knowledge, while irrelevant attributes are removed or ignored during the learning process. This paper investigates the relationship between attributional relevancy to decision classes and to learning systems. The experimental results from different databases show that some attributes relevant to decision classes may be irrelevant to the learning system. Experiments are performed on eight different databases using the C4.5 system for learning decision trees from examples.

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Authors and Affiliations

  1. Machine Learning and Inference Laboratory, George Mason University, 4400 University Dr., 22030, Fairfax, VA

    Ibrahim F. Imam

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  1. Ibrahim F. Imam
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Editor information

Zbigniew W. Raś Maciek Michalewicz

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© 1996 Springer-Verlag Berlin Heidelberg

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Imam, I.F. (1996). An empirical study on the incompetence of attribute selection criteria. In: Raś, Z.W., Michalewicz, M. (eds) Foundations of Intelligent Systems. ISMIS 1996. Lecture Notes in Computer Science, vol 1079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61286-6_170

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  • DOI: https://doi.org/10.1007/3-540-61286-6_170

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61286-5

  • Online ISBN: 978-3-540-68440-4

  • eBook Packages: Springer Book Archive

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