Abstract: A new associative classification algorithm based on rule ranking was proposed. The proposed method takes advantage of the optimal rule method preferring high duality rules. At the same time, it takes into consideration as many rules as possible, which can improve the bias of CBA that builds a classifier according to only several rules covering the training dataset. In the proposed algorithm, after the generation of association rules whose consectuences are class labels,rules arc ranked according to their length, confidence, support, lift and so on. Rules having no influence on the classification result arc deleted during ranking. The set of the ranked rules with a default class constructs the final classifier. Fina11y,20 datasets selected from UCI ML Repository was used to evaluate the performance of the method. The experimental results show that our method has higher average classification accuracy in comparison with CBA.

Key words: Classification,Association rules,Ranking