A new decision tree learning algorithm

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摘要：In order to improve the generalization ability of binary decision trees, a new learning algorithm, the MMDT algorithm, is presented. Based on statistical learning theory the generalization performance of binary decision trees is analyzed, and the assessment rule is proposed. Under the direction of the assessment rule, the MMDT algorithm is implemented. The algorithm maps training examples from an original space to a high dimension featurespace, and constructs a decision tree in it. In the feature space, a new decision node splitting criterion, the max-min rule, is used, and the margin of each decision node is maximized using a support vector machine, to improve the generalization performance. Experimental results show that the new learning algorithm is much superior to others such as C4. 5 and OC1.

原文链接:http://www.cqvip.com//QK/86045X/200506/20953169.html

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关键词：Algorithm Decision Learning earning Learn 机器学习 分层次决策 学习算法 统计学习理论 分裂标准