Building Accurate Associative Classifier Based on Closed Itemsets and Certainty Factor
Zhongjun Deng, Xuefeng Zheng, Wei Guo Song · 2009
The application of association rule mining to classification has led to a new family of classifiers which are often referred to as Associative Classifiers (ACs). An advantage of ACs is that they are rule-based and thus lend themselves to an easier interpretation. However, it is common knowledge that association rule mining typically yields a sheer number of rules defeating the purpose of a human readable model. Hence, selecting and ranking a small subset of high-quality rules without jeopardizing the classification accuracy is paramount. This article introduces a new method for building associative classifier. In this method, only association rules based on closed itemsets are used for constructing classifier. Furthermore, certainty factor is used for ranking rules in classifier. Experimental results show that the proposed associative classifier is effective.