Abstract
Data Mining is a popular knowledge discovery technique. In data mining decision trees are of the simple and powerful decision making models. One of the limitations in decision trees is towards the data source which they tackle. If data sources which are given as input to decision tree are of imbalance nature then the efficiency of decision tree drops drastically, we propose a decision tree structure which mimics human learning by performing balance of data source to some extent. In this paper, we propose a novel method based on sampling strategy. Extensive experiments, using C4.5 decision tree as base classifier, show that the performance measures of our method is comparable to state-of-the-art methods.
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Battula, B.P., Bhattacharyya, D., Prasad, C.V.P.R., Kim, Th. (2015). A Novel Prototype Decision Tree Method Using Sampling Strategy . In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9155. Springer, Cham. https://doi.org/10.1007/978-3-319-21404-7_7
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DOI: https://doi.org/10.1007/978-3-319-21404-7_7
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