caa a22 4500 60547785X CHVBK 20210128100403.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10994-014-5449-9 doi (NATIONALLICENCE)springer-10.1007/s10994-014-5449-9 An incremental piecewise linear classifier based on polyhedral conic separation [Elektronische Daten] [Gurkan Ozturk, Adil Bagirov, Refail Kasimbeyli] 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. The Author(s), 2014 Classification nationallicence Polyhedral conic separation nationallicence Nonsmooth nonconvex optimization nationallicence Discrete gradient method nationallicence Ozturk Gurkan Department of Industrial Engineering, Faculty of Engineering, Anadolu University, 26555, Eskisehir, Turkey aut Bagirov Adil School of Science, Information Technology and Engineering, Federation University Australia, 3353, Ballarat, VIC, Australia aut Kasimbeyli Refail Department of Industrial Engineering, Faculty of Engineering, Anadolu University, 26555, Eskisehir, Turkey aut Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 397-413 0885-6125 101:1-3<397 2015 101 10994 https://doi.org/10.1007/s10994-014-5449-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-014-5449-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ozturk Gurkan Department of Industrial Engineering, Faculty of Engineering, Anadolu University, 26555, Eskisehir, Turkey aut NATIONALLICENCE 700 1- Bagirov Adil School of Science, Information Technology and Engineering, Federation University Australia, 3353, Ballarat, VIC, Australia aut NATIONALLICENCE 700 1- Kasimbeyli Refail Department of Industrial Engineering, Faculty of Engineering, Anadolu University, 26555, Eskisehir, Turkey aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 397-413 0885-6125 101:1-3<397 2015 101 10994