ACPRISM: Associative classification based on PRISM algorithm

Abstract

Associative classification (AC) is an integration between association rules and classification tasks that aim to predict unseen samples. Several studies indicate that the AC algorithms produce more accurate results than classical data mining algorithms. However, current AC algorithms inherit from association rules two major drawbacks resulting in a massive set of generated rules, in addition to a very large number of models (classifiers). In response to these two drawbacks, a new AC algorithm based on PRISM algorithm (ACPRISM) is proposed which employs the power of the PRISM algorithm to decrease the number of generated rules.To investigate the efficiency and the performance of the proposed algorithm, five different algorithms were tested, namely FACA, CBA, MAC, PRISM and RIPPER. Two experiments were conducted on groundwater and 16 different well-known datasets using predictive accuracy (%), number of generated rules and time taken to build the model (learning times).Our experimental results show that the ACPRISM produced the lowest number of rules, and is much more efficient and more scalable than all considered algorithms with regard to learning times. Finally, the ACPRISM outperformed the CBA, MCAR, PRISM and RIPPER algorithms in terms of predictive accuracy, and produced comparable results to the FACA algorithm.