E-mail Spam Filtering Based on Support Vector Machines with Taguchi Method for Parameter Selection

Support Vector Machines (SVM) is a powerful classification technique in data mining and has been successfully applied to many real-world applications. Parameter selection of SVM will affect classification performance much during training process. However, parameter selection of SVM is usually identified by experience or grid search (GS). In this study, we use Taguchi method to make optimal approximation for the SVM-based E-mail Spam Filtering model. Six real-world mail data sets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the Taguchi method can find the effective model with high classification accuracy.

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