A Discretization Algorithm That Keeps Positive Regions of All the Decision Classes
Most of the existing discretization methods such as k-interval discretization, equal width and equal frequency methods do not take the dependencies of decision attributes on condition attributes into account. In this paper, we propose a discretization algorithm that can keep the dependencies of the decision attribute on condition attributes, or keep the positive regions of the partition of the decision attribute. In the course of inducing classification rules from a data set, keeping these dependencies can achieve getting the set of the least condition attributes and the highest classification precision.
KeywordsPositive Region Scaler Method Discretization Algorithm Decision Attribute Severe Acute Respiratory Syndrome
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- 1.Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proc. of the 12th International Conference on Machine Learning, pp. 194–202 (1995)Google Scholar
- 2.Kononenko, I.: Naive Bayes classifier and continuous attributes Informatica, vol. 16, pp. 1–8 (1992)Google Scholar
- 4.Fayyad, U.M.: On the Induction of Decision Trees for Multiple Concept Learning. Ph.D. thesis, University of Michigan (1991)Google Scholar
- 5.Fayyad, U.M., Irani, K.: Multiinterval discretization of continuous-valued attributes for classification learning. In: Proc. of the 12th International Joint Conference on Artificial Intelligence, pp. 1022–1027 (1993)Google Scholar
- 6.Kerber, R.: ChiMerge:discretization of numeric attribute [C]. In: Proceedings of the 10th National Conference on Artificial Intelligence (AAAI 1992), SanJose, CA, pp. 123–128 (1992)Google Scholar