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A Novel Approach using Expert Knowledge on Error based Pruning

Author

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  • Ali Mirza Mahmood

    (Acharya Nagarjuna University, Guntur, India;
    DMS SVH College of Engineering, Machilipatnam, India)

  • Mrithyumjaya Rao Kuppa

    (Vaagdevi College of Engineering, Warangal, India)

Abstract

Many traditional pruning methods assume that all the datasets are equally probable and equally important, so they apply equal pruning to all the datasets. However, in real-world classification problems, all the datasets are not equal and considering equal pruning rate during pruning tends to generate a decision tree with a large size and high misclassification rate.In this paper, we present a practical algorithm to deal with the data specific classification problem when there are datasets with different properties. Another key motivation of the data specific pruning in the paper is "trading accuracy and size". A new algorithm called Expert Knowledge Based Pruning (EKBP) is proposed to solve this dilemma. We proposed to integrate error rate, missing values and expert judgment as factors for determining data specific pruning for each dataset. We show by analysis and experiments that using this pruning, we can scale both accuracy and generalisation for the tree that is generated. Moreover, the method can be very effective for high dimensional datasets. We conduct an extensive experimental study on openly available 40 real world datasets from UCI repository. In all these experiments, the proposed approach shows considerably reduction of tree size having equal or better accuracy compared to several benchmark decision tree methods that are proposed in literature.

Suggested Citation

  • Ali Mirza Mahmood & Mrithyumjaya Rao Kuppa, 2012. "A Novel Approach using Expert Knowledge on Error based Pruning," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 1-11.
  • Handle: RePEc:wsi:jikmxx:v:11:y:2012:i:01:n:s0219649212500074
    DOI: 10.1142/S0219649212500074
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    References listed on IDEAS

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    1. Robert H. Michaelsen & Kathleen M. Swigger, 1994. "Analysis of the Effectiveness of Machine Learning in Determining Decision Rules for Executive Compensation Planning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 3(4), pages 263-278, December.
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