A new approach for rule extraction of expert system based on SVM

Based on the SVM's excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVC algorithm is adopted to obtain the Clustering Distribution Matrix of the sample data whose features have been chosen. Finally, hyper-rectangle rules are constructed using the Clustering Distribution Matrix. To make the rules more concise, and easier to explain, hyper-rectangle rules are simplified further by using rules combinations, dimension reduction and interval extension. In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples. The UCI datasets are used to validate the new method proposed in this paper, the results compared with other rules extraction methods show that the new approach is more effective. The new method is used to extract knowledge rules for aero-engine oil monitoring expert system, and the results show that the new method can effectively extract knowledge rules for expert system, and break through the bottleneck in expert system knowledge dynamic acquisition.