Improving Prediction by Weighting Class Association Rules

Associative classification presents various methods whose common characteristic is the class prediction from the class association rules (rules whose consequent one is one of the class modalities). According to and, this new approach offers better results than the traditional approaches based on rules such as the decision trees. It also offers a great flexibility with the unstructured data. However, this approach suffers from a huge mass of generated rules which leads to a waste of time and space. In this work, we propose a new associative classification method. This method is based on FCP-Growth-P, an algorithm which generates only class itemsets and integrates for pruning the specialization condition of Li. Thus one saves both execution time and storage space. The phase of classification is based on a reduced base of the most significant rules leading to each class, which ensures the speed of the method. Examples are classified using the results given by the vote of these various rules weighted by its quality measure.