Is an ordinal class structure useful in classifier learning?

In recent years, a number of machine learning algorithms have been developed for the problem of ordinal classification. These algorithms try to exploit, in one way or the other, the order information of the problem, essentially relying on the assumption that the ordinal structure of the set of class labels is also reflected in the topology of the instance space. The purpose of this paper is to investigate, on an experimental basis, the validity of this assumption. Moreover, we seek to answer the question to what extent existing techniques and learning algorithms for ordinal classification are able to exploit order information and which properties of these techniques are important in this regard.

[1]  Ian Witten,et al.  Data Mining , 2000 .

[2]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[3]  Johannes Fürnkranz,et al.  Round Robin Classification , 2002, J. Mach. Learn. Res..

[4]  Johannes Fürnkranz,et al.  Pairwise Classification as an Ensemble Technique , 2002, ECML.

[5]  Yoav Freund,et al.  Large Margin Classification Using the Perceptron Algorithm , 1998, COLT' 98.

[6]  M. Friedman The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[7]  Eibe Frank,et al.  A Simple Approach to Ordinal Classification , 2001, ECML.

[8]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[9]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[10]  Stefan Kramer,et al.  Ensembles of nested dichotomies for multi-class problems , 2004, ICML.

[11]  M. Narasimha Murty,et al.  Focused crawling with scalable ordinal regression solvers , 2007, ICML '07.

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[14]  Jaime S. Cardoso,et al.  Modelling ordinal relations with SVMs: An application to objective aesthetic evaluation of breast cancer conservative treatment , 2005, Neural Networks.

[15]  Hans-Peter Kriegel,et al.  Collaborative ordinal regression , 2006, ICML.

[16]  Jaime S. Cardoso,et al.  Learning to Classify Ordinal Data: The Data Replication Method , 2007, J. Mach. Learn. Res..

[17]  M. Friedman A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings , 1940 .

[18]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[19]  Wei Chu,et al.  New approaches to support vector ordinal regression , 2005, ICML.