A novel support vector regression for data set with outliers

• Support vector machine is sensitive to the outliers.
• A novel support vector regression together with fuzzification theory, inconsistency matrix and neighbors match operator is presented.
• The objective of this novel support vector regression is to increase the generalization ability for data set with outliers.

Support vector machine (SVM) is sensitive to the outliers, which reduces its generalization ability. This paper presents a novel support vector regression (SVR) together with fuzzification theory, inconsistency matrix and neighbors match operator to address this critical issue. Fuzzification method is exploited to assign similarities on the input space and on the output response to each pair of training samples respectively. The inconsistency matrix is used to calculate the weights of input variables, followed by searching outliers through a novel neighborhood matching algorithm and then eliminating them. Finally, the processed data is sent to the original SVR, and the prediction results are acquired. A simulation example and three real-world applications demonstrate the proposed method for data set with outliers.

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