CARs-RP: Lasso-based class association rules pruning
by Mohamed Azmi; Abdelaziz Berrado
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 18, No. 2, 2021

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.

Online publication date: Mon, 15-Feb-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email