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http://hdl.handle.net/1942/2809
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DC Field | Value | Language |
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dc.contributor.author | JANSSENS, Gerrit K. | - |
dc.contributor.author | Sorensen, K | - |
dc.contributor.author | LIMERE, Arthur | - |
dc.contributor.author | VANHOOF, Koen | - |
dc.date.accessioned | 2007-11-16T12:36:19Z | - |
dc.date.available | 2007-11-16T12:36:19Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS. p. 234-239 | - |
dc.identifier.isbn | 978-3-540-26076-9 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/1942/2809 | - |
dc.description.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. | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.relation.ispartofseries | LECTURE NOTES IN ARTIFICIAL INTELLIGENCE | - |
dc.title | Analysis of company growth data using genetic algorithms on binary trees | - |
dc.type | Journal Contribution | - |
local.bibliographicCitation.conferencename | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING | - |
dc.identifier.epage | 239 | - |
dc.identifier.spage | 234 | - |
local.format.pages | 6 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.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. | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.relation.ispartofseriesnr | 3518 | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1007/11430919_29 | - |
dc.identifier.isi | 000229956700028 | - |
item.fullcitation | JANSSENS, Gerrit K.; Sorensen, K; LIMERE, Arthur & VANHOOF, Koen (2005) Analysis of company growth data using genetic algorithms on binary trees. In: ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS. p. 234-239. | - |
item.contributor | JANSSENS, Gerrit K. | - |
item.contributor | Sorensen, K | - |
item.contributor | LIMERE, Arthur | - |
item.contributor | VANHOOF, Koen | - |
item.validation | ecoom 2006 | - |
item.accessRights | Closed Access | - |
item.fulltext | No Fulltext | - |
crisitem.journal.issn | 0302-9743 | - |
Appears in Collections: | Research publications |
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