Effects of SVM Parameter Optimization Based on the Parameter Design of Taguchi Method

Support Vector Machines (SVMs) are based on the concept of decision planes that define decision boundaries, and Least Squares Support Vector (LS-SVM) Machine is the reformulation of the principles of SVM. In this study a diagnosis on a BUPA liver disorders dataset, is conducted LS-SVM with the Taguchi method. The BUPA Liver Disorders dataset includes 345 samples with 6 features and 2 class labels. The system approach has two stages. In the first stage, in order to effectively determine the parameters of the kernel function, the Taguchi method is used to obtain better parameter settings. In the second stage, diagnosis of the BUPA liver disorders dataset is conducted using the LS-SVM classifier; the classification accuracy is 95.07%; the AROC is 99.12%. Compared with the results of related research, our proposed system is both effective and reliable.

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