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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2550

Title: Data mining with genetic algorithms on binary trees
Authors: Sorensen, K
JANSSENS, Gerrit
Issue Date: 2003
Publisher: ELSEVIER SCIENCE BV
Citation: EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 151(2). p. 253-264
Abstract: This paper focuses on the automatic interaction detection (AID)-technique, which belongs to the class of decision tree data mining techniques. The AID-technique explains the variance of a dependent variable through an exhaustive and repeated search of all possible relations between the (binary) predictor variables and the dependent variable. This search results in a tree in which non-terminal nodes represent the binary predictor variables, edges represent the possible values of these predictor variables and terminal nodes or leafs correspond to classes of subjects. Despite of being self-evident, the AID-technique has its weaknesses. To overcome these drawbacks a technique is developed that uses a genetic algorithm to find a set of diverse classification trees, all having a large explanatory power. From this set of trees, the data analyst is able to choose the tree that fulfils his requirements and does not suffer from the weaknesses of the AID-technique. The technique developed in this paper uses some specialised genetic operators that are devised to preserve the structure of the trees and to preserve high fitness from being destroyed. An implementation of the algorithm exists and is freely available. Some experiments were performed which show that the algorithm uses an intensification stage to find high-fitness trees. After that, a diversification stage recombines high-fitness building blocks to find a set of diverse solutions. (C) 2003 Elsevier B.V. All rights reserved.
Notes: Univ Antwerp, Fac Appl Econ Sci, B-2020 Antwerp, Belgium. Limburgs Univ Centrum, B-3590 Diepenbeek, Belgium.Sorensen, K, Univ Antwerp, Fac Appl Econ Sci, Middelheimlaan 1, B-2020 Antwerp, Belgium.
URI: http://hdl.handle.net/1942/2550
DOI: 10.1016/S0377-2217(02)00824-X
ISI #: 000185032100002
ISSN: 0377-2217
Category: A1
Type: Journal Contribution
Validation: ecoom, 2004
Appears in Collections: Research publications

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