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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2809

Title: Analysis of company growth data using genetic algorithms on binary trees
Authors: JANSSENS, Gerrit
Sorensen, K
LIMERE, Arthur
VANHOOF, Koen
Issue Date: 2005
Publisher: SPRINGER-VERLAG BERLIN
Citation: ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS. p. 234-239
Series/Report: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, 3518
Abstract: This paper investigates why some companies grow faster than others, by data mining a survey of a large number of companies in Flanders (the northern part of Belgium). Faster or slower average growth over a time period is explained by building a classification tree containing several categorical variables (both quantitative and qualitative). The technique used - called genAID - splits the population at different levels. It is inspired by the Automatic Interaction Detector (AID) technique to find trees that explain the variability in average growth but uses a genetic algorithm to overcome some of the drawbacks of AID. Classical AID or other tree-growing techniques usually generate a single tree for interpretation. This approach has been criticized because, due to the artifacts of data, spurious interactions may occur. genAID offers the user-analyst a set of trees, which are the best ones found over a number of generations of the genetic algorithm. The user-analyst is then offered the choice of choosing a tree by trading off explanatory power against either the ease of understanding or the conformity with an existing theory.
Notes: Univ Limburg Ctr, Fac Appl Econ, Data Anal & Modelling Res Grp, B-3590 Diepenbeek, Belgium. Univ Antwerp, Fac Appl Econ, B-2000 Antwerp, Belgium. Limburgs Univ Ctr, Fac Appl Econ, Financial Management Res Grp, B-3560 Diepenbeek, Belgium.Janssens, GK, Univ Limburg Ctr, Fac Appl Econ, Data Anal & Modelling Res Grp, B-3590 Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/2809
DOI: 10.1007/11430919_29
ISI #: 000229956700028
ISBN: 978-3-540-26076-9
ISSN: 0302-9743
Category: A1
Type: Journal Contribution
Validation: ecoom, 2006
Appears in Collections: Research publications

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