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An Enhanced Univariate Discretization Based on Cluster Ensembles

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Intelligent and Evolutionary Systems

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 5))

Abstract

Most discretization algorithms focus on the univariate case. In general, they take into account the target class or interval-wise frequency of data. In so doing, useful information regarding natural group, hidden pattern and correlation among the attributes may be inevitably lost. In response, this paper introduces a new pruning method that exploits natural groups or clusters as an explicit constraint to traditional cut-point determination techniques. This unsupervised approach makes use of cluster ensembles to reveal similarities between data belonging to adjacent intervals. To be precise, a cut-point between a pair of highly similar or related intervals will be dropped. This pruning mechanism is coupled with three different univariate discretization algorithms, with the evaluation is conducted on 10 datasets and 3 classifier models. The results suggest that the proposed method usually achieve higher classification accuracy levels, than those of the three baseline counterparts.

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Author information

Authors and Affiliations

  1. School of Information Technology, Mae Fah Luang University, Muang, Chiang Rai, 57100, Thailand

    Kittakorn Sriwanna & Natthakan Iam-On

  2. Royal Thai Air Force Academy, 171/1 Klongthanhon, Saimai, Bangkok, 10220, Thailand

    Tossapon Boongoen

Authors
  1. Kittakorn Sriwanna
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  2. Tossapon Boongoen
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  3. Natthakan Iam-On
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Corresponding author

Correspondence to Kittakorn Sriwanna .

Editor information

Editors and Affiliations

  1. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Kittichai Lavangnananda

  2. School of Computing and Informatics, Institut Teknologi Brunei, Gadong, Brunei Darussalam

    Somnuk Phon-Amnuaisuk

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Worrawat Engchuan

  4. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

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© 2016 Springer International Publishing Switzerland

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Sriwanna, K., Boongoen, T., Iam-On, N. (2016). An Enhanced Univariate Discretization Based on Cluster Ensembles. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-27000-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-27000-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26999-3

  • Online ISBN: 978-3-319-27000-5

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