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    Using the symmetrical Tau criterion for feature selection decision tree and neural network learning

    133988_133988.pdf (112.0Kb)
    Access Status
    Open access
    Authors
    Hadzic, Fedja
    Dillon, Tharam S
    Date
    2006
    Type
    Conference Paper
    
    Metadata
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    Citation
    Hadzic, Fedja and Dillon, Tharam S. 2006. Using the symmetrical Tau criterion for feature selection decision tree and neural network learning, in Huan Liu, Robert Stine and Leonardo Auslender (ed), 2nd Workshop on Feature Selection for Data Mining: Interfacing Machine Learning and Statistics, in conjunction with the 2006 SIAM International Conference on Data Mining, Apr 20 2006, pp. 107-114. Bethesda, USA: ACM.
    Source Title
    Proceedings of the 2nd workshop on feature selection for data mining: interfacing machine learning and statistics, in conjunction with the 2006 SIAM international conference on data mining
    Source Conference
    2nd Workshop on Feature Selection for Data Mining: Interfacing Machine Learning and Statistics, in conjunction with the 2006 SIAM International Conference on Data Mining
    ISBN
    9780898716115
    Faculty
    Curtin Business School
    The Digital Ecosystems and Business Intelligence Institute (DEBII)
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    URI
    http://hdl.handle.net/20.500.11937/31352
    Collection
    • Curtin Research Publications
    Abstract

    The data collected for various domain purposes usually contains some features irrelevant tothe concept being learned. The presence of these features interferes with the learning mechanism and as a result the predicted models tend to be more complex and less accurate. It is important to employ an effective feature selection strategy so that only the necessary and significant features will be used to learn the concept at hand. The Symmetrical Tau (t) [13] is a statistical-heuristic measure for the capability of an attribute in predicting the class of another attribute, and it has successfully been used as a feature selection criterion during decision tree construction. In this paper we aim to demonstrate some other ways of effectively using the t criterion to filter out the irrelevant features prior to learning (pre-pruning) and after the learning process (post-pruning). For the pre-pruning approach we perform two experiments, one where the irrelevant features are filtered out according to their t value, and one where we calculate the t criterion for Boolean combinations of features and use the highest t-valued combination. In the post-pruning approach we use the t criterion to prune a trained neural network and thereby obtain a more accurate and simple rule set. The experiments are performed on data characterized by continuous and categorical attributes and the effectiveness of the proposed techniques is demonstrated by comparing the derived knowledge models in terms of complexity and accuracy.

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