A Weighted Voting-Based Associative Classification Algorithm

A new associative classification algorithm based on weighted voting (ACWV) is presented. ACWV takes advantage of two methods: the optimal rule method preferring high-quality rules and the voting method considering the majority of the rules. Moreover, the method takes into account both the length and convictions of rules to calculate their weights. First, ACWV builds a class-count FPtree (called CCFP-tree) from the given historical data. After that, the weighted voting result for a new instance can be obtained from the CCFP-tree directly without storing, retrieving and sorting rules explicitly. The label of the class with maximal sum of weighted votes is then that of the new instance. Results of the experiments with 36 data sets selected from the UCI machine learning repository show that the proposed method has its advantages in comparison with previous methods in terms of classification accuracy.