Evaluation measures for ordinal regression

ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications

Volumn , Issue , 2009, Pages 283-287

  Baccianella, Stefano ^a   Esuli, Andrea ^a   Sebastiani, Fabrizio ^a  

^a CNR   (Italy)

Author keywords

Class imbalance; Evaluation measures; Ordinal classification; Ordinal regression; Product reviews

Indexed keywords

CLASS IMBALANCE; EVALUATION MEASURES; ORDINAL CLASSIFICATION; ORDINAL REGRESSION; PRODUCT REVIEWS;

INTELLIGENT SYSTEMS; STATISTICAL TESTS; STRUCTURAL OPTIMIZATION;

REGRESSION ANALYSIS;

EID: 77949512941     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISDA.2009.230     Document Type: Conference Paper

References (28)

1. W. Chu and S. S. Keerthi, "New approaches to support vector ordinal regression," in Proceedings of the 22nd International Conference on Machine Learning (ICML'05), Bonn, DE, 2005, pp. 145-152.
2. K. Crammer and Y. Singer, "Pranking with ranking," in Advances in Neural Information Processing Systems. Cambridge, US: The MIT Press, 2002, vol. 14, pp. 641-647.
3. A. Shashua and A. Levin, "Ranking with large margin principle: Two approaches," in Advances in Neural Information Processing Systems. Cambridge, US: The MIT Press, 2003, vol. 15, pp. 937-944.
4. B. Pang and L. Lee, "Opinion mining and sentiment analysis," Foundations and Trends in Information Retrieval, vol. 2, no. 1/2, pp. 1-135, 2008.
5. P. Beineke, T. Hastie, and S. Vaithyanathan, "The sentimental factor: Improving review classification via human-provided information," in Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL'04), Barcelona, ES, 2004, pp. 263-270.
6. J. Blitzer, M. Dredze, and F. Pereira, "Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification," in Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL'07), Prague, CZ, 2007, pp. 440-447.
7. K. Dave, S. Lawrence, and D. M. Pennock, "Mining the peanut gallery: Opinion extraction and semantic classification of product reviews," in Proceedings of the 12th International Conference on the World Wide Web (WWW'03), Budapest, HU, 2003, pp. 519-528.
8. A. Kennedy and D. Inkpen, "Sentiment classification of movie reviews using contextual valence shifters," Computational Intelligence, vol. 22, no. 2, pp. 110-125, 2006.
9. M. Koppel and J. Schler, "The importance of neutral examples for learning sentiment," Computational Intelligence, vol. 22, no. 2, pp. 100-109, 2006.
10. A.-M. Popescu and O. Etzioni, "Extracting product features and opinions from reviews," in Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP'05), Vancouver, CA, 2005, pp. 339-346.
11. S. Baccianella, A. Esuli, and F. Sebastiani, "Multi-facet rating of product reviews," in Proceedings of the 31st European Conference on Information Retrieval (ECIR'09), Toulouse, FR, 2009, pp. 461-472.
12. A. B. Goldberg and X. Zhu, "Seeing stars when there aren't many stars: Graph-based semi-supervised learning for sentiment categorization," in Proceedings of the HLT/NAACL Workshop on Graph-based Algorithms for Natural Language Processing, New York, US, 2006, pp. 45-52.
13. B. Pang and L. Lee, "Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales," in Proceedings of the 43rd Meeting of the Association for Computational Linguistics (ACL'05), Ann Arbor, US, 2005, pp. 115-124.
14. K. Shimada and T. Endo, "Seeing several stars: A rating inference task for a document containing several evaluation criteria," in Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), Osaka, JP, 2008, pp. 1006-1014.
15. N. Jindal and B. Liu, "Opinion spam and analysis," in Proceedings of the 1st International Conference on Web Search and Web Data Mining (WSDM'08), Palo Alto, US, 2008, pp. 219-229.
16. N. V. Chawla, N. Japkowicz, and A. Kolcz, "Editorial: Special issue on learning from imbalanced data sets," ACM SIGKDD Explorations, vol. 6, no. 1, pp. 1-6, 2004.
17. Y. Yang, "An evaluation of statistical approaches to text categorization," Information Retrieval, vol. 1, no. 1/2, pp. 69-90, 1999.
18. D. D. Lewis, "Evaluating and optmizing autonomous text classification systems," in Proceedings of the 18th ACM International Conference on Research and Development in Information Retrieval (SIGIR'95), Seattle, US, 1995, pp. 246-254.
19. B. Snyder and R. Barzilay, "Multiple aspect ranking using the good grief algorithm," in Proceedings of the Joint Conference of the North American Chapter of the Association for Computational Linguistics and Human Language Technology Conference (NAACL/HLT'07), Rochester, US, 2007, pp. 300-307.
20. J. Basilico and T. Hofmann, "Unifying collaborative and content-based filtering," in Proceedings of the 21st International Conference on Machine Learning (ICML'04), Banff, CA, 2004, pp. 65-72.
21. K. Dembczyński, W. Kotłowski, and R. Słowiń ski, "Ordinal classification with decision rules," in Proceedings of the ECML/PKDD'07 workshop on Mining Complex Data, Warsaw, PL, 2007, pp. 169-181.
22. R. K. Gopal and S. K. Meher, "Customer churn time prediction in mobile telecommunication industry using ordinal regression," in Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), Osaka, JP, 2008, pp. 884-889.
23. L. Li and H.-T. Lin, "Ordinal regression by extended binary classification," in Advances in Neural Information Processing Systems. Cambridge, US: The MIT Press, 2007, vol. 19, pp. 865-872.
24. E. Frank and M. Hall, "A simple approach to ordinal classification," in Proceedings of the 12th European Conference on Machine Learning (ECML'01), Freiburg, DE, 2001, pp. 145-156.
25. L. Gaudette and N. Japkowicz, "Evaluation methods for ordinal classification," in Proceedings of the 22nd Canadian Conference on Artificial Intelligence, Kelowna, CA, 2009, pp. 207-210.
26. W. Waegeman, B. De Baets, and L. Boullart, "ROC analysis in ordinal regression learning," Pattern Recognition Letters, vol. 29, no. 1, pp. 1-9, 2008.
27. F. Sebastiani, "Machine learning in automated text categorization," ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.
28. V. Vapnik, Statistical Learning Theory. New York, US: Wiley, 1998.