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Meta-Level Analysis of Decision Tree Induction

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Meta-Learning in Decision Tree Induction

Part of the book series: Studies in Computational Intelligence ((SCI,volume 498))

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Abstract

Object oriented design divides complex algorithms and data structures into smaller and simpler components, specializing in solving extracted subproblems. As a result, also in the approach to a general framework for DT induction, the algorithms can be composed by a number of compatible components. In the framework described in Chap. 3, even the simplest DT induction algorithms are composed of several components responsible for such tasks as performing search process, estimating split quality, pruning and so on.

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Notes

  1. 1.

    The example has been prepared especially for this illustration. It has not been published in any article as an approach claiming to be right.

  2. 2.

    Since the counts in Table 5.4 are summed over 5 algorithms, the maximum possible value is \(5*21=105\). The highest score of 56 means wins in more than half of the tests.

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Authors and Affiliations

  1. Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, ul. Grudziądzka 5, 87-100, Toruń, Poland

    Krzysztof Grąbczewski

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  1. Krzysztof Grąbczewski
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Correspondence to Krzysztof Grąbczewski .

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Grąbczewski, K. (2014). Meta-Level Analysis of Decision Tree Induction. In: Meta-Learning in Decision Tree Induction. Studies in Computational Intelligence, vol 498. Springer, Cham. https://doi.org/10.1007/978-3-319-00960-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-00960-5_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-00960-5

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