Generic Associative Classification Rules: A Comparative Study
Ines Bouzouita, Samir Elloumi · 2011
Associative classification is a supervised classification approach, integrating association mining and classification. Several studies in data mining have shown that associative classification achieves higher classification accuracy than do traditional classification techniques. However, the associative classification suffers from a major drawback: The huge number of the generated classification rules which takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we have proposed an associative classification method that reduces associative classification rules without jeopardizing the classification accuracy. Moreover, we will introduce in this paper two different strategies to classify new instances based on some interestingness measures that arise from data mining literature in order to select the best rules during classification. A detailed description of this method is presented in this paper, as well as the experimentation study on 12 benchmark data sets proving that our approach is highly competitive in terms of accuracy in comparison with popular classification approaches.