Abstract
Text categorization is a key task in information retrieval and natural language processing. Providing a reliability measure of the classification result for a text document into a particular category can benefit the recognition rate as well as better inform the user with regard to the confidence that should be attributed to the output. A novel reliability measure is proposed starting from running different binary classifiers in the Error-Correcting Output Codes (ECOC) framework. Documents classified in a particular category which have a higher ECOC-computed distance from their classification in the next ranked category also have a higher associated reliability. This is the main idea explored in the proposed ECOC-based text classifier with a reject option. Experiments performed for some commonly used text categorization benchmark datasets demonstrate the potential of the proposed method.
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References
Feldman, R., Sanger, J.: The Text Mining Handbook - Advanced Approaches in Analyzing Unstructured Data, pp. I-XII, 1–410. Cambridge University Press (2007)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1) (2002)
Hotho, A., Nurnberger, A., Paass, G.: A Brief Survey of Text Mining. LDV Forum 20(1), 19–62 (2005)
Fumera, G., Pillai, I., Roli, F.: Classification with reject option in text categorisation systems. In: Proc. 12th International Conference on Image Analysis and Processing, pp. 582–587. IEEE Computer Society (2003)
Fumera, G., Pillai, I., Roli, F.: A Two-Stage Classifier with Reject Option for Text Categorisation. In: Structural, Syntactic, and Statistical Patt. Rec., pp. 771–779 (2004)
Theeramunkong, T., Sriphaew, K.: Discovery of Relations among Scientific Articles using Association Rule Mining. In: Proceedings of the 2007 NSTDA Annual Conference Science (Science and Technology for National Productivity and Happiness), Thailand Science Park, Pathumthani, Thailand (2007)
Pillai, I., Fumera, G., Roli, F.: A Classification Approach with a Reject Option for Multi-label Problems. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011, Part I. LNCS, vol. 6978, pp. 98–107. Springer, Heidelberg (2011)
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error correcting output codes. J. of Artificial Intelligence Research 2, 263–286 (1995)
Kołcz, A., Chowdhury, A.: Improved Naive Bayes for Extremely Skewed Misclassification Costs. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 561–568. Springer, Heidelberg (2005)
Smirnov, E.N., Nalbantov, G.I., Kaptein, A.M.: Meta-conformity approach to reliable classification. Intell. Data Anal. 13(6), 901–915 (2009)
Kaptein, A.M.: Meta-Classifier Approaches to Reliable Text Classification, Master Thesis, Universiteit Maastricht, The Netherlands (2005)
Allwein, E.L., Shapire, R.E., Singer, Y.: Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research 1, 113–141 (2000)
Hastie, T., Tibshirani, R.: Classification by pairwise grouping. The Annals of Stat. 26(5), 451–471 (1998)
Lin, S., Costello, D.J.: Error Control Coding, 2nd edn. Prentice-Hall, Inc. (2004)
Hatami, N.: Thinned-ECOC ensemble based on sequential code shrinking. Expert Systems with Applications 39(1) (2012)
Pujol, O., Radeva, P., Vitria, J.: Discriminant ECOC: A heuristic method for application dependent design of error correcting output codes. IEEE Transactions on PAMI 28(6), 1001–1007 (2006)
Pujol, O., Escalera, S., Radeva, P.: An incremental node embedding technique for error correcting output codes. Pattern Recognition 41, 713–725 (2008)
Zhou, J., Peng, H., Suen, C.Y.: Data-driven decomposition for multi-class classification. Pattern Recognition 41, 67–76 (2008)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research 5, 361–397 (2004)
Kosmopoulos, A., Gaussier, E., Paliouras, G., Aseervatham, S.: The ECIR 2010 Large Scale Hierarchical Classification, Workshop report (2010)
Silla Jr., C.N., Freitas, A.A.: A Survey of Hierarchical Classification Across Different Application Domains. Data Mining and Knowledge Discovery 20(1) (2010)
Armano, G., Chira, C., Hatami, N.: Error-Correcting Output Codes for Multi-label Text Categorization. In: Proceedings of 3rd Italian Information Retrieval Workshop (IIR 2012), Bari (in press, 2012)
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Armano, G., Chira, C., Hatami, N. (2012). Ensemble of Binary Learners for Reliable Text Categorization with a Reject Option. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_13
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DOI: https://doi.org/10.1007/978-3-642-28942-2_13
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