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.
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Janssens, G.K., Sösrensen, K., Limère, A., Vanhoof, K. (2005). Analysis of Company Growth Data Using Genetic Algorithms on Binary Trees. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_29
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DOI: https://doi.org/10.1007/11430919_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
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