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Title: Pareto-optimality of oblique decision trees from evolutionary algorithms
Authors: Maria Pangilinan, Jose
Issue Date: 2011
Publisher: SPRINGER
Source: JOURNAL OF GLOBAL OPTIMIZATION, 51(2). p. 301-311
Abstract: This paper investigates the performance of evolutionary algorithms in the optimization aspects of oblique decision tree construction and describes their performance with respect to classification accuracy, tree size, and Pareto-optimality of their solution sets. The performance of the evolutionary algorithms is analyzed and compared to the performance of exhaustive (traditional) decision tree classifiers on several benchmark datasets. The results show that the classification accuracy and tree sizes generated by the evolutionary algorithms are comparable with the results generated by traditional methods in all the sample datasets and in the large datasets, the multiobjective evolutionary algorithms generate better Pareto-optimal sets than the sets generated by the exhaustive methods. The results also show that a classifier, whether exhaustive or evolutionary, that generates the most accurate trees does not necessarily generate the shortest trees or the best Pareto-optimal sets.
Notes: [Pangilinan, JM] St Louis Univ, Baguio, Philippines [Janssens, GK] Hasselt Univ, IMOB, Diepenbeek, Belgium;
Keywords: Pareto-optimality; Decision tree; Evolutionary algorithm; Multiobjective optimization; Classification; Data mining
Document URI:
ISSN: 0925-5001
e-ISSN: 1573-2916
DOI: 10.1007/s10898-010-9614-9
ISI #: 000294470200009
Category: A1
Type: Journal Contribution
Validations: ecoom 2012
Appears in Collections:Research publications

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