Article ID: | iaor20132542 |
Volume: | 64 |
Issue: | 5 |
Start Page Number: | 775 |
End Page Number: | 785 |
Publication Date: | May 2013 |
Journal: | Journal of the Operational Research Society |
Authors: | Jeong M K, Park J I, Kim N, Shin K S |
Keywords: | heuristics: genetic algorithms |
In this paper, we propose a novel multiphase support vector regression (mp‐SVR) technique to approximate a true relationship for the case where the effect of input on output changes abruptly at some break‐points. A new formulation for mp‐SVR is presented to allow such structural changes in regression function. And then, we present a new hybrid‐encoding scheme in genetic algorithms to select the best combination of the kernel functions and to determine both break‐points and hyperparameters of mp‐SVR. The proposed method has a major advantage over the conventional ones that different kernel functions can be possibly adapted to different regions of the data domain. Computational results in two examples including a real‐life data demonstrate its capability in capturing the local characteristics of the data more effectively. Consequently, the mp‐SVR has a high potential value in a wide range of applications for function approximations.