An experimental study of three different rule ranking formulas in associative classification

Associative classification (AC) is a combination of classification and association rule in data mining that has attracted several scholars due to its models simplicity and its effectiveness in predicting test cases. This paper investigates the impact of rule ranking before constructing the classifier in AC mining. We would like to experimentally compare three different rule ranking formulas during building the classifier in order to determine the most appropriate one than can positively impact the classification accuracy of the derived classifiers. We believe that rule ranking may play a significant role in determining accuracy of the classifiers and also can be considered a prepruning step for the rules. Sixteen different data sets from UCI data repository have been used in the experiments, and the bases of the comparisons are the error rate, and the number of rules. The results reveal that rule ranking plays a major role in determining the subset of rules to be utilised in the prediction step and it indeed affects the predictive power of such subset.