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28 March 2005 Oblique decision tree induction using multimembered evolution strategies
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Abstract
A new construction algorithm for binary oblique decision tree classifier, MESODT, is described. Multimembered evolution strategies (μ,λ) integrated with the perceptron algorithm is adopted as the optimization algorithm to find the appropriate split that minimizes the evaluation function at each node of a decision tree. To better explore the benefits of this optimization algorithm, two splitting rules, the criterion based on the concept of degree of linear separability, and one of the traditional impurity measures -- information gain, are each applied to MESODT. The experiments conducted on public and artificial domains demonstrate that the trees generated by MESODT have, in most cases, higher accuracy and smaller size than the classical oblique decision trees (OC1) and axis-parallel decision trees (See5.0). Comparison with (1+1) evolution strategies is also described.
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Kun Zhang, Zujia Xu, and Bill P. Buckles "Oblique decision tree induction using multimembered evolution strategies", Proc. SPIE 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, (28 March 2005); https://doi.org/10.1117/12.596766
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