Recent studies in data mining have proposed a new classification approach, called associative classification, which, according to several reports, such as [7, 6], achieves higher classification accuracy than traditional classification approaches such as C4.5. However, the approach also suffers from two major deficiencies: (1) it generates a very large number of association rules, which leads to high processing overhead; and (2) its confidence-based rule evaluation measure may lead to overfitting. In comparison with associative classification, traditional rule-based classifiers, such as C4.5, FOIL and RIPPER, are substantially faster but their accuracy, in most cases, may not be as high. In this paper, we propose a new classification approach, CPAR (Classification based on Predictive Association Rules), which combines the advantages of both associative classification and traditional rule-based classification. Instead of generating a large number of candidate rules as in associative classification, CPAR adopts a greedy algorithm to generate rules directly from training data. Moreover, CPAR generates and tests more rules than traditional rule-based classifiers to avoid missing important rules. To avoid overfitting, CPAR uses expected accuracy to evaluate each rule and uses the best k rules in prediction.
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