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

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