Advances in Decision Sciences
Volume 2011 (2011), Article ID 515978, 13 pages
Research Article

Classification Performance for Making Decisions about Products Missing from the Shelf

ELTRUN, Department of Management Science and Technology, Athens University of Economics and Business, 47 Evelpidon and Lefkados Street, 113 62 Athens, Greece

Received 1 December 2010; Accepted 7 April 2011

Academic Editor: David Bulger

Copyright © 2011 Dimitris Papakiriakopoulos and Georgios Doukidis. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The out-of-shelf problem is among the most important retail problems. This work employs two different classification algorithms, C4.5 and naïve Bayes, in order to build a mechanism that makes decisions about whether a product is available on a retail store shelf or not. Following the same classification methods and feature spaces, we examined the classification performance of the algorithms in four different retail chains and utilized ROC curves and the area under curve measure to compare the predictive accuracy. Based on the results obtained for the different retail chains, we identified certain approaches for the development and introduction of such a mechanism in different retail contexts.