Rule Extraction for Problems with Hybrid Type Attributes

To hurdle the major drawback of neural network, this paper developed researches on rule extraction. For problems with continuous-valued and discrete-valued attributes, the paper present an approach to extract understandable rules. Rules extracted are comprehensible not only for discrete value but also for continuous value. Our experiment results on real-word dataset validate our approach and show that rules extracted by our approach are comprehensible.