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Impurity Measurement in Selecting Decision Node Tree that Tolerate Noisy Cases

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Recent Advances in Information and Communication Technology 2017 (IC2IT 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 566))

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Abstract

In a recent years, recommending an appropriate attribute of binary decision tree under unusual circumstances – such as training or testing with noisy attribute, has become more challenge in researching. Since, most of traditional impurity measurements have never been tested how much they can tolerate with encountered noisy cases. Consequently, this paper studies and proposes an impurity measurement which can be used to evaluate the goodness of binary decision tree node split under noisy situation, accurately. In order to make sure that the accuracy of decision tree classification by using the proposed measurement has been yet preserved, setting up an experiment to compare with the traditional impurity measures was conducted. And the result shows that accuracy of the proposed measurement in classifying a class under noisy case is acceptable.

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Author information

Authors and Affiliations

  1. Information Technology Laboratory, Vincent Mary School of Science and Technology, Assumption University, Bangkok, Thailand

    Benjawan Srisura

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  1. Benjawan Srisura
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Correspondence to Benjawan Srisura .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Phayung Meesad

  2. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Sunantha Sodsee

  3. Lehrgebiet Kommunikationsnetze, FernUniversität in Hagen, Hagen, Germany

    Herwig Unger

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Srisura, B. (2018). Impurity Measurement in Selecting Decision Node Tree that Tolerate Noisy Cases. In: Meesad, P., Sodsee, S., Unger, H. (eds) Recent Advances in Information and Communication Technology 2017. IC2IT 2017. Advances in Intelligent Systems and Computing, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-319-60663-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-60663-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60662-0

  • Online ISBN: 978-3-319-60663-7

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