ACRIPPER: A New Associative Classification Based on RIPPER Algorithm

Abstract

Associative Classification (AC) classifiers are of substantial interest due to their ability to be utilised for mining vast sets of rules. However, researchers over the decades have shown that a large number of these mined rules are trivial, irrelevant, redundant, and sometimes harmful, as they can cause decision-making bias. Accordingly, in our paper, we address these challenges and propose a new novel AC approach based on the RIPPER algorithm, which we refer to as ACRIPPER. Our new approach combines the strength of the RIPPER algorithm with the classical AC method, in order to achieve: (1) a reduction in the number of rules being mined, especially those rules that are largely insignificant; (2) a high level of integration among the confidence and support of the rules on one hand and the class imbalance level in the prediction phase on the other hand. Our experimental results, using 20 different well-known datasets, reveal that the proposed ACRIPPER significantly outperforms the well-known rule-based algorithms RIPPER and J48. Moreover, ACRIPPER significantly outperforms the current AC-based algorithms CBA, CMAR, ECBA, FACA, and ACPRISM. Finally, ACRIPPER is found to achieve the best average and ranking on the accuracy measure.

A Comparative Study for Different Resampling Techniques for Imbalanced datasets

“…Sara et al [13] used oversampling and under sampling to check the impact of balancers on the performance of classifiers. The classifiers are named RIPPER [23] , multi-layer perceptron (MLP) [24] , k-nearest neighbors (KNN) [25], and C4.5 [26] decision tree classifiers. They used datasets from the SEER program for specific types of cancer.…”