Rough ν-support vector regression

Authors:
Yongping Zhao;Jianguo Sun
Affiliations:
Department of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China;Department of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

After combining the classical @n-SVR with the rough theory, we propose a rough @n-SVR. Double @es are utilized to construct the rough margin for rough @n-SVR instead of single @e for the classical @n-SVR, and this rough margin consisting of positive region, boundary region, and negative region yields the feasible set of the rough @n-SVR larger than that of the classical @n-SVR, which makes the objective function of the rough @n-SVR not more than that of the classical @n-SVR. This may lead to the improvement of the performance. Meantime, experimental results on benchmark data sets confirm the validation and feasibility of our proposed rough @n-SVR.