Abstract
Textual Feature Selection (TFS) is an important phase in the process of text classification. It aims to identify the most significant textual features (i.e. key words and/or phrases), in a textual dataset, that serve to distinguish between text categories. In TFS, basic techniques can be divided into two groups: linguistic vs. statistical. For the purpose of building a language-independent text classifier, the study reported here is concerned with statistical TFS only. In this paper, we propose a novel statistical TFS approach that hybridizes the ideas of two existing techniques, DIAAF (Darmstadt Indexing Approach Association Factor) and RS (Relevancy Score). With respect to associative (text) classification, the experimental results demonstrate that the proposed approach can produce greater classification accuracy than other alternative approaches.
This is a preview of subscription content, log in via an institution to check access.
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
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Database. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA, May 1993, pp. 207–216. ACM Press, New York (1993)
Ali, K., Manganaris, S., Srikant, R.: Partial Classification using Association Rules. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA, USA, August 1997, pp. 115–118. AAAI Press, Menlo Park (1997)
Antonie, M.-L., Zaïane, O.R.: Text Document Categorization by Term Association. In: Proceedings of the 2002 IEEE International Conference on Data Mining, Maebashi City, Japan, December 2002, pp. 19–26. IEEE Computer Society, Los Alamitos (2002)
Church, K.W., Hanks, P.: Word Association Norms, Mutual Information, and Lexicography. In: Proceedings of the 27th Annual Meeting on Association for Computational Linguistics, Vancouver, BC, Canada, pp. 76–83. Association for Computational Linguistics (1989)
Coenen, F., Leng, P.: An Evaluation of Approaches to Classification Rule Selection. In: Proceedings of the 4th IEEE International Conference on Data Mining, Brighton, UK, November 2004, pp. 359–362. IEEE Computer Society, Los Alamitos (2004)
Coenen, F., Leng, P., Zhang, L.: Threshold Tuning for Improved Classification Association Rule Mining. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 216–225. Springer, Heidelberg (2005)
Coenen, F., Leng, P.: The Effect of Threshold Values on Association Rule based Classification Accuracy. Journal of Data and Knowledge Engineering 60(2), 345–360 (2007)
Coenen, F., Leng, P., Sanderson, R., Wang, Y.J.: Statistical Identification of Key Phrases for Text Classification. In: Proceedings of the 5th International Conference on Machine Learning and Data Mining, Leipzig, Germany, July 2007, pp. 838–853. Springer, Heidelberg (2007)
Cohen, W.W.: Fast Effective Rule Induction. In: Proceedings of the 12th International Conference on Machine Learning, Tahoe City, CA, USA, July 1995, pp. 115–123. Morgan Kaufmann Publishers, San Francisco (1995)
Deng, Z.-H., Tang, S.-W., Yang, D.-Q., Zhang, M., Wu, X.-B., Yang, M.: Two odds-radio-based Text Classification Algorithms. In: Proceedings of the Third International Conference on Web Information Systems Engineering workshop, Singapore, December 2002, pp. 223–231. IEEE Computer Society, Los Alamitos (2002)
Fano, R.M.: Transmission of Information ( A Statistical Theory of Communication. The MIT Press, Cambridge (1961)
Fragoudis, D., Meretaskis, D., Likothanassis, S.: Best Terms: An Efficient Feature-selection Algorithm for Text Categorization. Knowledge and Information Systems 8(1), 16–33 (2005)
Fuhr, N.: Models for Retrieval with Probabilistic Indexing. Information Processing and Management 25(1), 55–72 (1989)
Fuhr, N., Buckley, C.: A Probabilistic Learning Approach for Document Indexing. ACM Transactions on Information System 9(3), 223–248 (1991)
Hersh, W.R., Buckley, C., Leone, T.J., Hickman, D.H.: OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, July 1994, pp. 192–201. ACM/Springer (1994)
Kobayashi, M., Aono, M.: Vector Space Models for Search and Cluster Mining. In: Berry, M.W. (ed.) Survey of Text Mining – Clustering, Classification, and Retrieval, pp. 103–122. Springer, New York (2004)
Lang, K.: News Weeder: Learning to Filter Netnews. In: Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, CA, USA, July 1995, pp. 331–339. Morgan Kaufmann Publishers, San Francisco (1995)
Li, X., Liu, B.: Learning to Classify Texts using Positive and Unlabeled Data. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, Acapulco, Mexico, August 2003, pp. 587–594. Morgan Kaufmann Publishers, San Francisco (2003)
Li, W., Han, J., Pei, J.: CMAR: Accurate and Efficient Classification based on Multiple Class-association Rules. In: Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, CA, USA, November-December 2001, pp. 369–376. IEEE Computer Society, Los Alamitos (2001)
Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, August 1998, pp. 80–86. AAAI Press, Menlo Park (1998)
Maron, M.E.: Automatic Indexing: An Experimental Inquiry. Journal of the ACM 8(3), 404–417 (1961)
Moschitti, A., Basili, R.: Complex Linguistic Features for Text Classification: A Comprehensive Study. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 181–196. Springer, Heidelberg (2004)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)
Salton, G., Buckley, C.: Term-weighting Approaches in Automatic Text Retrieval. Information Processing & Management 24(5), 513–523 (1988)
Salton, G., Wong, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Information Retrieval and Language Processing 18(11), 613–620 (1975)
Scheffer, T., Wrobel, S.: Text Classification Beyond the Bag-of-words Representation. In: Proceedings of the Workshop on Text Learning, held at the Nineteenth International Conference on Machine Learning, Sydney, Australia (2002)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Shidara, Y., Nakamura, A., Kudo, M.: CCIC: Consistent Common Itemsets Classifier. In: Proceedings of the 5th International Conference on Machine Learning and Data Mining, Leipzig, Germany, July 2007, pp. 490–498. Springer, Heidelberg (2007)
Wang, Y.J., Coenen, F., Leng, P., Sanderson, R.: Text Classification using Language-independent Pre-processing. In: Proceedings of the Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Peterhouse College, Cambridge, UK, December 2006, pp. 413–417. Springer, Heidelberg (2006)
Wang, Y.J., Sanderson, R., Coenen, F., Leng, P.: Document-base Extraction for Single-label Text Classification. In: Proceedings of the 10th International Conference on Data Warehousing and Knowledge Discovery, Turin, Italy, September 2008, pp. 357–367. Springer, Heidelberg (2008)
Wiener, E., Pedersen, J.O., Weigend, A.S.: A Neural Network Approach to Topic Spotting. In: Proceedings of the 4th Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, NV, USA, April 1995, pp. 317–332 (1995)
Yin, X., Han, J.: CPAR: Classification based on Predictive Association Rules. In: Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, CA, USA, May 2003, pp. 331–335. SIAM, Philadelphia (2003)
Yoon, Y., Lee, G.G.: Practical Application of Associative Classifier for Document Classification. In: Proceedings of the Second Asia Information Retrieval Symposium, Jeju Island, Korea, October 2005, pp. 467–478. Springer, Heidelberg (2005)
Zaïane, O.R., Antonie, M.-L.: Classifying Text Documents by Associating Terms with Text Categories. In: Proceedings of the 13th Australasian Database Conference, Melbourne, Victoria, Australia, January-February 2002, pp. 215–222. CRPIT 5 Australian Computer Society (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Y.J., Li, F., Coenen, F., Sanderson, R., Xin, Q. (2010). Hybrid DIAAF/RS: Statistical Textual Feature Selection for Language-Independent Text Classification. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-14400-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14399-1
Online ISBN: 978-3-642-14400-4
eBook Packages: Computer ScienceComputer Science (R0)