A Hybrid Method for Extracting Classification Rules
Chuanli Zhuang, Zetian Fu, Daoliang Li · 2006
Neural networks is considered the most powerful classifier and rough set theory is thought of the best to reduce attributes and to generate rules. The combination of neural networks and rough set is very useful for knowledge acquires. Integrating of the advantages of two approaches and having solved the data continuous problem, this paper presents a hybrid method to extract classification rules. There are three models in our method, in first model, neural networks was employed to classify the data sets. In the second model, the continuous attributes are discretized and the self-organizing neural network was applied to ensure result consistent before and after the discretization. In the third model, rough sets theory was used to reduce attributes and generate the rules. The proposed approach was applied on abandoned mine wastes data and the extracted rules was testified based on the analysis of case studies, the result show that the method was reasonable.