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Random Forest of Oblique Decision Trees for ERP Semi-automatic Configuration

  • Thanh-Nghi Do
  • Sorin Moga
  • Philippe Lenca
Part of the Studies in Computational Intelligence book series (SCI, volume 551)

Abstract

Enterprise Resource Planning (ERP) is one of the most important parts of company’s information system. However few ERP implementation projects are delivered on time. Configuration of ERP based on questionnaires and/or interviews is time consuming and expensive, especially because many answers should be checked and corrected by ERP consultants. Supervised learning algorithms can thus be useful to automatically detect wrong and correct answers. Comparison done on real free open-source ERP data shows that random forest of oblique decision trees is very efficient.

Keywords

ERP configuration free text classification random forest of oblique decision trees ERP5 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.UMR 6285 Lab-STICC, Technopôle Brest-IroiseInstitut Mines-Telecom; Telecom BretagneBrest Cedex 3France
  2. 2.College of Information TechnologyCan Tho UniversityCan ThoVietnam
  3. 3.Université Européenne de BretagneRennesFrance

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