Abstract
In this paper, a hybrid learning approach named Flexible NBTree is proposed. Flexible NBTree uses Bayes measure δ to select proper test and applies post-discretization strategy to construct decision tree. The finial decision tree nodes contain univariate splits as regular decision trees, but the leaf nodes contain General Naive Bayes, which is a variant of standard Naive Bayesian classifier. Empirical studies on a set of natural domains show that Flexible NBTree has clear advantages with respect to the generalization ability when compared against its counterpart, NBTree.
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Wang, L., Yuan, S., Li, L., Li, H. (2004). Improving the Performance of Decision Tree: A Hybrid Approach. In: Atzeni, P., Chu, W., Lu, H., Zhou, S., Ling, TW. (eds) Conceptual Modeling – ER 2004. ER 2004. Lecture Notes in Computer Science, vol 3288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30464-7_26
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DOI: https://doi.org/10.1007/978-3-540-30464-7_26
Publisher Name: Springer, Berlin, Heidelberg
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