Abstract
The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper explores the notion of kernel trick and empirical feature space in order to reformulate the most widely used linear ordinal classification algorithm (the Proportional Odds Model or POM) to perform nonlinear decision regions. The proposed method seems to be competitive with other state-of-the-art algorithms and significantly improves the original POM algorithm when using 8 ordinal datasets. Specifically, the capability of the methodology to handle nonlinear decision regions has been proven by the use of a non-linearly separable toy dataset.
This work has been partially subsidized by the TIN2011-22794 project of the Spanish Ministerial Commission of Science and Technology (MICYT), FEDER funds and the P2011-TIC-7508 project of the “Junta de Andalucía” (Spain).
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Pérez-Ortiz, M., Gutiérrez, P.A., Cruz-Ramírez, M., Sánchez-Monedero, J., Hervás-Martínez, C. (2013). Kernelizing the Proportional Odds Model through the Empirical Kernel Mapping. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_26
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DOI: https://doi.org/10.1007/978-3-642-38679-4_26
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