Experiments with an innovative tree pruning algorithm

Authors:
Mingyu Zhong;Michael Georgiopoulos;Georgios C. Anagnostopoulos
Affiliations:
School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL;School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL;Department of Electrical and Computer Engineering, Florida Institute of Technology, Melbourne, FL
Venue:
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Year:
2007

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Abstract

The pruning phase is one of the necessary steps in decision 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 experimental 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.