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A standard data set is useful to empirically evaluate classification rules learning algorithms. However, there is still no standard data set which is common enough for various situations. Data sets from the real world are limited to specific applications. The sizes of attributes, the rules and samples of the real data are fixed. A data generator is proposed here to produce synthetic data set which...
Decision tree is a landmark approach to automatic decision support. From a visualize decision tree, people can easily understand how the rules are produced. In this paper, the objective function is treated as an attribute of the classification rules. The proposed algorithm determines the value of the objective function firstly, and then determines other attribute values one by one to form a classification...
The application of information entropy to decision tree algorithms has been shown to produce very accurate classifiers. Information entropy is utilized to ensure that the average distance of paths from the non-leaf node to each descendant leaf node of the decision tree is shortest. Therefore, it works well for data set which covers all the underlying rules. But it is lack of prediction ability when...
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