Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The nature of statistical learning theory
The nature of statistical learning theory
Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Nomograms for visualization of naive Bayesian classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
The class imbalance problem in learning classifier systems: a preliminary study
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Measuring and Mitigating the Costs of Stockouts
Management Science
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
AUC: a better measure than accuracy in comparing learning algorithms
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
| Hi-index | 12.05 |
Product availability is an important component to maintain consumer satisfaction and secure revenue streams for the retailer and the product supplier. Empirical research suggests that products missing from the shelf, also called 'out-of-shelf', is a frequent phenomenon. One of the challenges is to identify products missing from the shelf on a daily base without conducting physical store audit. Through empirical evaluation, this study compares various classification algorithms that can identify 'out-of-shelf' products, which is the minority class of product availability. Due to the class imbalance of product availability, an ensemble learning method is used to increase performance of the base classifiers used. The validation results indicate that it is possible to deliver accurate predictions regarding which products are 'out-of-shelf' for a selected retail store on a daily base. However, the predictions could not identify a significant number of the products missing from the shelf.