Induced Rule-Based Fuzzy Inference System from Support Vector Machine Classifier for Anomalous Propagation Echo Detection

Hansoo Lee, Yeongsang Jeong, Sungshin Kim
2016 International Journal of Machine Learning and Computing  
Support vector machine is a popular classifier to find an optimal hyperplane which separates given data into two groups. Due to its remarkable performance, the support vector machine is applied in various fields such as inductive inferences, classifications or regressions. By its black box characteristics, there are plenty of actively discussed researches about analyzing trained support vector machine classifier. In this paper, we propose a method to make a fuzzy inference system using
more » ... rules from the support vector machine. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, a support vector machine is implemented. After that, a decision tree is generated. And it is used to implement fuzzy inference system by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, the performances of both classification methods showed similar results. Further, we can determine the inner structure of the support vector machine. Index Terms-Support vector machine, rule extraction, fuzzy inference system, anomalous propagation echo.
doi:10.18178/ijmlc.2016.6.2.579 fatcat:zxytnhaxsfaa5edca6qljitkti