Abstract
Text Categorization (TC) has attracted the attention of the research community in the last decade. Algorithms like Support Vector Machines, Naïve Bayes or k Nearest Neighbors have been used with good performance, confirmed by several comparative studies. Recently, several ensemble classifiers were also introduced in TC. However, many of those can only provide a category for a given new sample. Instead, in this paper, we propose a methodology – MECAC – to build an ensemble of classifiers that has two advantages to other ensemble methods: 1) it can be run using parallel computing, saving processing time and 2) it can extract important statistics from the obtained clusters. It uses the mean co-association matrix to solve binary TC problems. Our experiments revealed that our framework performed, on average, 2.04% better than the best individual classifier on the tested datasets. These results were statistically validated for a significance level of 0.05 using the Friedman Test.
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References
Yang, Y., Pedersen, J.: A Comparative Study on Feature Selection in Text Categorization. In: ICML 1997, pp. 412–420 (1997)
Yang, Y., Liu, X.: A Re-Examination of Text Categorization Methods. In: 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42–49 (1999)
Yang, Y.: An Evaluation of Statistical Approaches to Text Categorization. Information Retrieval 1, 69–90 (1999)
Colas, F., Brazdil, P.: Comparison of SVM and Some Older Classification Algorithms in Text Classification Tasks. In: Artificial Intelligence in Theory and Practice, pp. 169–178 (2006)
Cho, S., Lee, J.: Learning Neural Network Ensemble for Practical Text Classification. In: Liu, J., Cheung, Y.-m., Yin, H. (eds.) IDEAL 2003. LNCS, vol. 2690, pp. 1032–1036. Springer, Heidelberg (2003)
Bi, Y., Bell, D.A., Wang, H., Guo, G., Greer, K.: Combining Multiple Classifiers Using Dempster’s Rule of Combination for Text Categorization. In: Torra, V., Narukawa, Y. (eds.) MDAI 2004. LNCS (LNAI), vol. 3131, pp. 127–138. Springer, Heidelberg (2004)
Zhang, T., Oles, F.: Text Categorization Based on Regularized Linear Classification Methods. Information Retrieval 4, 5–31 (2001)
Monti, S., Tamayo, P., Mesirov, J., Golub, T.: Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Machine Learning 52, 91–118 (2003)
Bottcher, M., Hoppner, F., Spiliopoulou, M.: On Exploiting the Power of Time in Data Mining. SIGKDD Explor. Newsl. 10, 3–11 (2008)
http://www.daviddlewis.com/resources/testcollections/reuters21578/
Khan, A., Baharudin, B., Lee, L., Khan, K.: A Review of Machine Learning Algorithms for Text-Documents Classification. Journal of Advances in Information Technology 1 (2010)
Joachims, T.: A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. In: 14th International Conference on Machine Learning, ICML 1997, pp. 143–151 (1997)
Nardiello, P., Sebastiani, F., Sperduti, A.: Discretizing Continuous Attributes in AdaBoost for Text Categorization. Advances in Information Retrieval (2003)
Dunn, J.: Well-Separated Clusters and Optimal Fuzzy Partitions. Journal of Cybernetics 4, 95–104
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On Clustering Validation Techniques. Journal of Intelligent Information Systems 17, 107–145 (2001)
Meila, M.: Comparing clusterings–an information based distance. Journal of Multivariate Analysis 98, 873–895 (2007)
R Development Core Team: R: A Language and Environment for Statistical Computing., Vienna, Austria (2005)
Salton, G., Allan, J., Buckley, C., Singhal, A.: Automatic analysis, theme generation, and summarization of machine-readable texts. Readings in Information Retrieval, 478–483 (1997)
Rogati, M., Yang, Y.: High-performing feature selection for text classification. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, pp. 659–661. ACM, McLean (2002)
Venables, W., Ripley, B.: Modern Applied Statistics with S, New York, USA (2002)
Chang, C., Lin, C.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 1–27 (2011)
Hornik, K., Buchta, C., Zeileis, A.: Open-source machine learning: R meets Weka. Computational Statistics 24, 225–232 (2009)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)
Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20, 37–46 (1960)
Iman, R., Davenport, J.: Approximations of the critical region of the Friedman statistic. Communications in Statistics 571–595 (1980)
Yang, Y., Zhang, J., Carbonell, J., Jin, C.: Topic-conditioned novelty detection. In: 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada, pp. 688–693 (2002)
Mendes-Moreira, J., Jorge, A.M., Soares, C., de Sousa, J.F.: Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach. In: Perner, P. (ed.) MLDM 2009. LNCS, vol. 5632, pp. 191–205. Springer, Heidelberg (2009)
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Moreira-Matias, L., Mendes-Moreira, J., Gama, J., Brazdil, P. (2012). Text Categorization Using an Ensemble Classifier Based on a Mean Co-association Matrix. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_41
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DOI: https://doi.org/10.1007/978-3-642-31537-4_41
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