Classification Performance for Making Decisions about Products Missing from the Shelf | Zendy

Dimitris Papakiriakopoulos | Zendy; Georgios I. Doukidis | Zendy

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Classification Performance for Making Decisions about Products Missing from the Shelf

Author(s) -

Dimitris Papakiriakopoulos,

Georgios I. Doukidis

Publication year - 2011

Publication title -

advances in decision sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.178

H-Index - 13

eISSN - 2090-3367

pISSN - 2090-3359

DOI - 10.1155/2011/515978

Subject(s) - naive bayes classifier , computer science , measure (data warehouse) , product (mathematics) , order (exchange) , bayes' theorem , off the shelf , mechanism (biology) , work (physics) , feature (linguistics) , operations research , data mining , machine learning , artificial intelligence , business , bayesian probability , mathematics , support vector machine , geometry , mechanical engineering , philosophy , linguistics , software engineering , finance , epistemology , engineering

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

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