Title: CARs-RP: Lasso-based class association rules pruning

Authors: Mohamed Azmi; Abdelaziz Berrado

Addresses: AMIPS Research Team, EMI, Mohammed V University of Rabat, B.P. 765, Rabat Agdal, Morocco ' AMIPS Research Team, EMI, Mohammed V University of Rabat, B.P. 765, Rabat Agdal, Morocco

Abstract: Classification based on association rules gets more and more interest in research and practice. In many contexts, rules are often mined from sparse data in high-dimensional spaces, which leads to large number of rules with considerable containment and overlap. Pruning is often used in search for an optimal subset of rules. This paper introduces a method for class association rules (CARs) pruning. It learns weights for a set of CARs by maximising the likelihood function subject to the sum of the absolute values of the weights. The pruning strength is controlled by a shrinkage parameter ⋋. The suggested method allows the user to choose the appropriate subset of CARs. This is achieved based on a trade-off between the accuracy and complexity of the resulting classifier which is controlled by changing ⋋. Experimental analysis shows that the introduced method allows to build more concise classifiers with comparable accuracy to other methods.

Keywords: class association rules; CARs; pruning; regularisation; Lasso; rules weighting; associative classification.

DOI: 10.1504/IJBIDM.2021.112991

International Journal of Business Intelligence and Data Mining, 2021 Vol.18 No.2, pp.197 - 217

Received: 02 Mar 2018
Accepted: 28 Jun 2018

Published online: 28 Jan 2021 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article