The Composite Rule Induction System proposed by Liang (1992) that uses the tabular approach and statistical inference to process qualitative and quantitative attributes separately for generating better classification rules. Yang (2007) extended the method by incorporating the second-order rules. This Study further extends the previous method by including a mechanism for detecting the existence of interaction effects. The detection method checks the degree of independence between attributes to determine whether the second-order rules should be processed. In order to evaluate the performance of the proposed method, an enhanced prototype system was developed and both real and simulated data were used to compare its accuracy and rule complexity with existing systems. The result shows that the enhanced system performs at least as accurate as the existing system but is significantly better in the complexity of the resulting knowledge base.