Multi-class associative classification based on intersection method and extended chi-square testing
一种基于正交法和扩展卡方检验的关联分类算法

Given that there exit some defects in several typical classification algorithms, such as poor classification performance and long running time when the algorithm efficiency is not high, this paper proposed a classification algorithm ERAC based on intersection method and extended chi-square testing. This algorithm first produced, through intersection method, all the frequency items and association rules. Then it conducted classification and pruning on rules using an extended testing method, reducing the number of rules for classification effectively. Subsequent experiments prove that new method, compared with the CBA algorithm, has higher classification accuracy and operating efficiency.