An incremental piecewise linear classifier based on polyhedral conic separation

WOS: 000361624700018
In this paper, a piecewise linear classifier based on polyhedral conic separation is developed. This classifier builds nonlinear boundaries between classes using polyhedral conic functions. Since the number of polyhedral conic functions separating classes is not known a priori, an incremental approach is proposed to build separating functions. These functions are found by minimizing an error function which is nonsmooth and nonconvex. A special procedure is proposed to generate starting points to minimize the error function and this procedure is based on the incremental approach. The discrete gradient method, which is a derivative-free method for nonsmooth optimization, is applied to minimize the error function starting from those points. The proposed classifier is applied to solve classification problems on 12 publicly available data sets and compared with some mainstream and piecewise linear classifiers.
Australian Research Council [DP140103213]
The authors would like to thank three anonymous referees for their criticism and comments which allowed to improve the quality of the paper. The research by Dr. A.M. Bagirov was supported under Australian Research Council's Discovery Projects funding scheme (Project No. DP140103213).