M. Zhong, M. Georgiopoulos, and G.C. Anagnostopoulos (USA)
decision tree, classification, 2-norm, pruning, CART, C4.5
The pruning phase is one of the necessary steps in deci sion tree induction. Existing pruning algorithms tend to have some or all of the following difficulties: 1) lack of theoretical support; 2) high computational complexity; 3) dependence on validation; 4) complicated implementation. The 2-norm pruning algorithm proposed here addresses all of the above difficulties. This paper demonstrates the ex perimental results of the comparison among the 2-norm pruning algorithm and two classical pruning algorithms, the Minimal Cost-Complexity algorithm (used in CART) and the Error-based pruning algorithm (used in C4.5), and confirms that the 2-norm pruning algorithm is superior in accuracy and speed.
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