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Mining Intelligence and Knowledge Exploration pp 38–49Cite as

An Empirical Comparison of Discretization Methods for Neural Classifier

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8284))

Abstract

Discretization leads the improvement in classification accuracy and generalizes the problem well for further knowledge extraction. As a result researchers have developed various discretization methods for preprocessing the data. This paper provides a survey of existing discretization methods that preprocess the data for datamining. Also the paper evaluates the effectiveness of various discretization methods in terms of better discretization scheme and better accuracy of classification by comparing the performance of some traditional and recent discetization algorithms on six different real datasets namely Iris, Ionosphere, Waveform-5000, Wisconsin breast cancer, Hepatitis Domain and Pima Indian Diabetes. The feedforward neural network with conjugate gradient training algorithm is used to compute the accuracy of classification from the data discretized by those algorithms.

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Author information

Authors and Affiliations

  1. Department of Computer Applications, Sarah Tucker College, Tirunelveli, 627007, TN, India

    M. Gethsiyal Augasta

  2. Department of Computer Science, V.H.N.S.N College, VirudhuNagar, 626001, TN, India

    Thangairulappan Kathirvalavakumar

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  1. M. Gethsiyal Augasta
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  2. Thangairulappan Kathirvalavakumar
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Editor information

Editors and Affiliations

  1. Department of Business Information Systems, FSIC, National University of Ireland, University College Cork, O’Rahilly Buildings, Cork, Ireland

    Rajendra Prasath

  2. Research Centre in Computer Science, V.H.N.Senthikumara Nadar College (Autonomous), 626 001, Virudhunagar, Tamil Nadu, India

    T. Kathirvalavakumar

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Augasta, M.G., Kathirvalavakumar, T. (2013). An Empirical Comparison of Discretization Methods for Neural Classifier. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-03844-5_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03843-8

  • Online ISBN: 978-3-319-03844-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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