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Proceedings Paper

Combining evolutionary algorithms with oblique decision trees to detect bent-double galaxies
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Paper Abstract

Decision tress have long been popular in classification as they use simple and easy-to-understand tests at each node. Most variants of decision trees test a single attribute at a node, leading to axis- parallel trees, where the test results in a hyperplane which is parallel to one of the dimensions in the attribute space. These trees can be rather large and inaccurate in cases where the concept to be learned is best approximated by oblique hyperplanes. In such cases, it may be more appropriate to use an oblique decision tree, where the decision at each node is a linear combination of the attributes. Oblique decision trees have not gained wide popularity in part due to the complexity of constructing good oblique splits and the tendency of existing splitting algorithms to get stuck in local minima. Several alternatives have been proposed to handle these problems including randomization in conjunction wiht deterministic hill-climbing and the use of simulated annealing. In this paper, we use evolutionary algorithms (EAs) to determine the split. EAs are well suited for this problem because of their global search properties, their tolerance to noisy fitness evaluations, and their scalability to large dimensional search spaces. We demonstrate our technique on a synthetic data set, and then we apply it to a practical problem from astronomy, namely, the classification of galaxies with a bent-double morphology. In addition, we describe our experiences with several split evaluation criteria. Our results suggest that, in some cases, the evolutionary approach is faster and more accurate than existing oblique decision tree algorithms. However, for our astronomical data, the accuracy is not significantly different than the axis-parallel trees.

Paper Details

Date Published: 13 October 2000
PDF: 9 pages
Proc. SPIE 4120, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III, (13 October 2000); doi: 10.1117/12.403609
Show Author Affiliations
Erick Cantu-Paz, Lawrence Livermore National Lab. (United States)
Chandrika Kamath, Lawrence Livermore National Lab. (United States)

Published in SPIE Proceedings Vol. 4120:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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