A novel support vector regression for data set with outliers |
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| Affiliation: | 1. Department of Electrical and Computer Engineering, Seoul National University, Republic of Korea;2. Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Republic of Korea;3. Automation and Systems Research Institute, Seoul National University, Republic of Korea;4. Advanced Institutes of Convergence Technology, Republic of Korea;1. Department of Instrumentation & Control, SVIT, Vasad, Gujarat, India;2. Department of Instrumentation & Control, Nirma University, Ahmedabad, Gujarat, India;1. Université du Québec en Outaouais, 101 Saint-Jean-Bosco, Gatineau, QC J8X 3X7, Canada;2. University of Ottawa, 800 King Edward, Ottawa, ON K1N 6N5, Canada |
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| Abstract: | 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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| Keywords: | Support vector regression Outlier Fuzzification theory Inconsistency matrix Neighbors match operator |
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