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Combining Evolutionary Algorithms With Oblique Decision Trees to Detect Bent Double Galaxies.

DE200415006501

Publication Date	2000
Personal Author	Cantu-Paz, E.; Kamath, C.
Page Count	16
Abstract	Decision trees 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 learnt 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 with deterministic hill climbing and the use of simulated annealing. In this paper, they 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. They demonstrate the technique on a practical problem from astronomy, namely, the classification of galaxies with a bent-double morphology, and describe their experiences with several split evaluation criteria
Keywords	Algorithms Astronomy Galaxies Decision tree analysis Dimensions Neural networks
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Lawrence Livermore National Lab., CA.; Department of Energy, Washington, DC.
Supplemental Notes	Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200416

Combining Evolutionary Algorithms With Oblique Decision Trees to Detect Bent Double Galaxies.

Combining Evolutionary Algorithms With Oblique Decision Trees to Detect Bent Double Galaxies.
DE200415006501

Publication Date	2000
Personal Author	Cantu-Paz, E.; Kamath, C.
Page Count	16
Abstract	Decision trees 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 learnt 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 with deterministic hill climbing and the use of simulated annealing. In this paper, they 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. They demonstrate the technique on a practical problem from astronomy, namely, the classification of galaxies with a bent-double morphology, and describe their experiences with several split evaluation criteria
Keywords	Algorithms Astronomy Galaxies Decision tree analysis Dimensions Neural networks
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Lawrence Livermore National Lab., CA.; Department of Energy, Washington, DC.
Supplemental Notes	Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200416