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IJMLC 2013 Vol. 3(1): 154-157 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.292

Support Vector Machines for Multi-Attribute ABC Analysis

Hasan Basri Kartal and Ferhan Cebi

Abstract—This paper examined the classification performance of Support Vector Machines (SVMs) on multi-criteria inventory analysis. The ABC analysis using the Simple Additive Weighting (SAW) method was employed to determine inventory classes of items held in inventory of a large scale automobile company operating in Turkey. The provided data set was analyzed with SVMs to obtain classification performance of the SVM learning algorithm. The results showed that SVM is highly applicable to the inventory classification problem.

Index Terms—ABC analysis, multi-criteria inventory classification, support vector machine (SVM).

The authors are with Faculty of Management, Istanbul Technical University, Istanbul, Turkey (e-mail: basrikartal@gmail.com; cebife@itu.edu.tr).


Cite:Hasan Basri Kartal and Ferhan Cebi, "Support Vector Machines for Multi-Attribute ABC Analysis," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 154-157, 2013.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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