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|Title:||Construction of Near-Optimal Axis-Parallel Decision Trees Using a Differential-Evolution-Based Approach|
|Author:||RAFAEL RIVERA LOPEZ|
|Abstract:||Inthispaper, adifferential-evolution-based approach implementing a global search strategy to ﬁnd a near-optimal axis-parallel decision tree is introduced. In this paper, the internal nodes of a decision tree are encoded in a real-valued chromosome, and a population of them evolves using the training accuracy of each one as its ﬁtness value. The height of a complete binary decision tree whose number of internal nodes is not less than the number of attributes in the training set is used to compute the chromosome size, and a procedure to map a feasible axis-parallel decision tree from one chromosome is applied, which uses both the smallest-position-value rule and the training instances. The best decision tree in the ﬁnal population is reﬁned replacing some leaf nodes with sub-trees to improve its accuracy. The differential evolution algorithm has been successfully applied in conjunction with several supervised learning methods to solve numerous classiﬁcation problems, due to it exhibiting a good tradeoff between its exploitation and exploration skills, and to the best of our knowledge, it has not been utilized to build axis-parallel decision trees. To obtain reliable estimates of the predictive performance of this approach and to compare its results with those achieved by other methods, a repeated stratiﬁed ten-fold cross-validation procedure is applied in the experimental study. A statistical analys is of these results suggests that our approach is better asadecision tree induction method as compared with other supervised learning methods. Al soour results are comparable to those obtained with random forest and one multilayer-perceptron-based classiﬁer.|
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