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ORCA: A Matlab/Octave toolbox for ordinal regression

Sanchez-Monedero, Javier, Gutierrez, Pedro A. and Perez-Ortiz, Maria 2019. ORCA: A Matlab/Octave toolbox for ordinal regression. Journal of Machine Learning Research 20 (125) , pp. 1-5.

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Abstract

Ordinal regression, also named ordinal classification, studies classification problems where there exist a natural order between class labels. This structured order of the labels is crucial in all steps of the learning process in order to take full advantage of the data. ORCA (Ordinal Regression and Classification Algorithms) is a Matlab/Octave framework that implements and integrates different ordinal classification algorithms and specifically designed performance metrics. The framework simplifies the task of experimental comparison to a great extent, allowing the user to: (i) describe experiments by simple configuration files; (ii) automatically run different data partitions; (iii) parallelize the executions; (iv) generate a variety of performance reports and (v) include new algorithms by using its intuitive interface. Source code, binaries, documentation, descriptions and links to data sets and tutorials (including examples of educational purpose) are available at https://github.com/ayrna/orca.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Journalism, Media and Culture
Publisher: Journal of Machine Learning Research
ISSN: 1532-4435
Date of First Compliant Deposit: 4 November 2019
Date of Acceptance: 23 July 2019
Last Modified: 07 Nov 2019 13:43
URI: http://orca-mwe.cf.ac.uk/id/eprint/126565

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