Classification Performance for Making Decisions about Products Missing from the Shelf

The out-of-shelf problem is among the most important retail problems. This work employs two different classification algorithms, C4.5 and naı̈ve 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.