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.