ORCA: A Matlab/Octave Toolbox for Ordinal Regression

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.

[1]  Gianluca Pollastri,et al.  A neural network approach to ordinal regression , 2007, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[2]  Ling Li,et al.  Reduction from Cost-Sensitive Ordinal Ranking to Weighted Binary Classification , 2012, Neural Computation.

[3]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[4]  Pedro Antonio Gutiérrez,et al.  Metrics to guide a multi-objective evolutionary algorithm for ordinal classification , 2014, Neurocomputing.

[5]  Ling Li,et al.  Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice , 2006, ALT.

[6]  Pedro Antonio Gutiérrez,et al.  Projection-Based Ensemble Learning for Ordinal Regression , 2014, IEEE Transactions on Cybernetics.

[7]  Chih-Jen Lin,et al.  A Comparison of Methods for Multi-class Support Vector Machines , 2015 .

[8]  Willem Waegeman,et al.  An ensemble of Weighted Support Vector Machines for Ordinal Regression , 2007 .

[9]  Pedro Antonio Gutiérrez,et al.  Ordinal Regression Methods: Survey and Experimental Study , 2016, IEEE Transactions on Knowledge and Data Engineering.

[10]  P. McCullagh.  Regression Models for Ordinal Data , 1980 .

[11]  Xiaoming Zhang,et al.  Kernel Discriminant Learning for Ordinal Regression , 2010, IEEE Transactions on Knowledge and Data Engineering.

[12]  Qinghua Zheng,et al.  Ordinal extreme learning machine , 2010, Neurocomputing.

[13]  Wei Chu,et al.  Support Vector Ordinal Regression , 2007, Neural Computation.

[14]  Pedro Antonio Gutiérrez,et al.  Partial order label decomposition approaches for melanoma diagnosis , 2018, Appl. Soft Comput..