- Title
- An incremental piecewise linear classifier based on polyhedral conic separation
- Creator
- Ozturk, Gurkan; Bagirov, Adil; Kasimbeyli, Refail
- Date
- 2015
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/97780
- Identifier
- vital:10254
- Identifier
-
https://doi.org/10.1007/s10994-014-5449-9
- Identifier
- ISSN:0885-6125
- Abstract
- 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. © 2014, The Author(s).
- Publisher
- Kluwer Academic Publishers
- Relation
- Machine Learning Vol. 101, no. 1-3 (2015), p. 397-413; http://purl.org/au-research/grants/arc/DP140103213
- Rights
- Copyright © 2014, The Author(s).
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Science; Classification; Discrete gradient method; Nonsmooth nonconvex optimization; Polyhedral conic separation; Errors; Gradient methods; Piecewise linear techniques; Separation; Derivative-free methods; Incremental approach; Nonlinear boundary; Nonsmooth optimization; Piecewise-linear classifier; Separating functions; Classification (of information)
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