Abstract
Ordinal classification is a form of multi-class classification where there is an inherent ordering between the classes, but not a meaningful numeric difference between them. Little attention has been paid as to how to evaluate these problems, with many authors simply reporting accuracy, which does not account for the severity of the error. Several evaluation metrics are compared across a dataset for a problem of classifying user reviews, where the data is highly skewed towards the highest values. Mean squared error is found to be the best metric when we prefer more (smaller) errors overall to reduce the number of large errors, while mean absolute error is also a good metric if we instead prefer fewer errors overall with more tolerance for large errors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Frank, E., Hall, M.: A simple approach to ordinal classification. Technical Report 01/05, Department of Computer Science, University of Waikato (2001)
Pang, B., Lee, L.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (ACL 2005) (2005)
Provost, F., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing induction algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998) (1998)
Wu, H., Lu, H., Ma, S.: A practical SVM-based algorithm for ordinal regression in image retrieval. In: Proceedings of the eleventh ACM international conference on Multimedia (MM 2003) (2003)
Waegeman, W., Baets, B.D., Boullart, L.: ROC analysis in ordinal regression learning. Pattern Recognition Letters 29, 1–9 (2008)
Ferri, C., Hernández-Orallo, J., Modroiu, R.: An experimental comparison of performance measures for classification. Pattern Recognition Letters 30, 27–38 (2009)
Yao, Y.Y.: Measuring retrieval effectiveness based on user preference of documents. Journal of the American Society for Information Science 46, 133–145 (1995)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL 2007) (2007)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gaudette, L., Japkowicz, N. (2009). Evaluation Methods for Ordinal Classification. In: Gao, Y., Japkowicz, N. (eds) Advances in Artificial Intelligence. Canadian AI 2009. Lecture Notes in Computer Science(), vol 5549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01818-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-01818-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01817-6
Online ISBN: 978-3-642-01818-3
eBook Packages: Computer ScienceComputer Science (R0)