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Using Resampling Techniques for Better Quality Discretization

  • Conference paper
Machine Learning and Data Mining in Pattern Recognition (MLDM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5632))

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Abstract

Many supervised induction algorithms require discrete data, however real data often comes in both discrete and continuous formats. Quality discretization of continuous attributes is an important problem that has effects on accuracy, complexity, variance and understandability of the induction model. Usually, discretization and other types of statistical processes are applied to subsets of the population as the entire population is practically inaccessible. For this reason we argue that the discretization performed on a sample of the population is only an estimate of the entire population. Most of the existing discretization methods, partition the attribute range into two or several intervals using a single or a set of cut points. In this paper, we introduce two variants of a resampling technique (such as bootstrap) to generate a set of candidate discretization points and thus, improving the discretization quality by providing a better estimation towards the entire population. Thus, the goal of this paper is to observe whether this type of resampling can lead to better quality discretization points, which opens up a new paradigm to construction of soft decision trees.

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Authors and Affiliations

  1. Laboratory ERIC, University of Lyon 2, 5, Avenue Pierre Mendes, 69676, Bron Cedex, France

    Taimur Qureshi & Djamel A. Zighed

Authors
  1. Taimur Qureshi
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  2. Djamel A. Zighed
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Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Körnerstr. 10, 04107, Leipzig, Deutschland, Germany

    Petra Perner

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Qureshi, T., Zighed, D.A. (2009). Using Resampling Techniques for Better Quality Discretization. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-03070-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03069-7

  • Online ISBN: 978-3-642-03070-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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