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Abstract: Machine learning methods for classification problems commonly assume that the class values are unordered. However, in many practical applications the class values do exhibit a natural order -- for example, when learning how to grade. The standard approach to ordinal classification converts the class value into a numeric quantity and applies a regression learner to the transformed data, translating the output back into a discrete class value in a post-processing step. A disadvantage of this... (Update)
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BibTeX entry: (Update)
E. Frank and M. Hall. A simple approach to ordinal classification. In Proceedings of the European Conference on Machine Learning, pages 145--165, 2001. http://citeseer.ist.psu.edu/frank01simple.html More
@article{ frank01simple,
author = "Eibe Frank and Mark Hall",
title = "A Simple Approach to Ordinal Classification",
journal = "Lecture Notes in Computer Science",
volume = "2167",
pages = "145+",
year = "2001",
url = "citeseer.ist.psu.edu/frank01simple.html" }
Citations (may not include all citations):2177 Programs for Machine Learning (context) - Quinlan - 1993
262 Data Mining: Practical Machine Learning Tools and Techniques.. (context) - Witten, Frank - 2000
183 Solving multiclass learning problems via errorcorrecting out.. - Dietterich, Bakiri - 1995
51 Wrappers for Performance Enhancement and Oblivious Decision .. - Kohavi - 1995 ACM
7 Making better use of global discretization - Frank, Witten - 1999 ACM DBLP
5 Regression models for ordinal data: A machine learning appro.. - Herbrich, Graepel et al. - 1999
3 Prediction of ordinal classes using regression trees - Kramer, Widmer et al. - 2001 ACM DBLP
1 Regression/DataSets (context) - Torgo, Sets et al. - 2001
1 Decision trees for ordinal classication (context) - Potharst, Bioch - 2000
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