Evolutionary learning of document categories

Information Retrieval

Volumn 10, Issue 1, 2007, Pages 69-83

  Serrano, J I ^a   Del Castillo, M D ^a  

^a Consejo Superior de Investigaciones Científicas   (Spain)

Author keywords

Distance based methods; Genetic algorithms; Text categorization

Indexed keywords

EID: 33846517684     PISSN: 13864564     EISSN: 15737659     Source Type: Journal    
DOI: 10.1007/s10791-006-9012-6     Document Type: Article

References (24)

1. del, Castillo M. D., & Serrano, J. I. (2004). A multistrategy approach for digital text categorization from imbalanced documents. ACM SIGKDD Explorations, 6, 70-79.
2. Cohen, W. W., & Singer, Y. (1999). Context-sensitive learning methods for text categorization. ACM Trans. Inform. Systems, 17(2), 141-173.
3. Cohen, W. W. (1995). Learning to classify English text with ILP methods. In L. De Raedt, (Ed.), Advances logic programming (pp. 124-143). Amsterdam: IOS Press.
4. Doan, A., Domingos, P., & Halevy, A. (2003). Learning to match the schemas of data sources: a multistrategy approach. Machine Learning, 50, 279-301.
5. Dumais, S. T., Platt, J., Heckerman, D., & Sahami, M. (1998). Inductive learning algorithms and representation for text categorization. In Proceedings of the CIKM-98, 7th International Conference on Information and Knowledge Management (pp. 148-155). Bethesda.
6. Goldberg, D. (1989). Genetic algorithms in search, optimization & machine learning, (ed.) Addison-Wesley Publishing Company, Inc.
7. Godoy, D., & Amandi, A. (2000). PersonalSearcher: an intelligent agent for searching web pages (pp. 43-52). LNAI, 1952. Springer-Verlag.
8. Grobelnik, M., & Mladenic, D. (1998). Efficient text categorization. In text mining workshop on the 10th european conference on machine learning (pp. 1-10). Chemnitz.
9. Han Eui-Hong, S., Karypis, G., & Kumar, V. (2001). Text categorization using weight adjusted k-nearest neighbor classification. In PAKDD'2001 (pp. 53-65). Springer-Verlag, LNAI 2035.
10. Hull, D. A. (1994). Improving text retrieval for the routing problem using latent semantic indexing. In Proceedings of SIGIR-94, 17th ACM International Conference on Research and Development in Information Retrieval (pp. 282-289). Dublin.
11. Joachims, T. (1998). Text categorization with support vector machines. In Proceedings of ECML-98 10th European Conference on Machine Learning (pp. 137-142). Chemnitz.
12. Lenz, M., Hubner, A., & Kunze, M. (1998). Textual CBR. In M. Lenz, B. Bartsch, B. D. Burkhard, and S. Wess (Eds.), Case-based reasoning technology (pp. 115-138). Springer-Verlag, LNAI 1400.
13. Lewis, D. D. (1998). Naïve Bayes at forty: The independence assumption in information retrieval. In Proceedings of ECML-98, 10th European Conference on Machine Learning (pp. 4-15). Germany.
14. Lewis, D. D., & Gale, W. A. (1994). Heterogeneous uncertainty sampling for supervised learning. In Proceedings of SIGIR-94, 11th International Conference on Research and Development in Information Retrieval (pp. 3-12). Dublin.
15. Lewis, D. D., & Ringuette, M. (1994). A comparison of two learning algorithms for text categorization. In Proceedings of SDAIR-94, 3rd. Annual Symposium on Document Analysis and Information Retrieval (pp. 81-93). Las Vegas.
16. Mitchell, T. M. (1997) Machine learning. The McGraw- Hill Companies.
17. Porter, M. F. (1980) An algorithm for suffix stripping. Program, 14(3), 130-137.
18. Ritcher, M. M. (1995). The knowledge contained in similarity measures. In Invited Talk at ICCBR-95.
19. Ruiz, M. E., & Srinivasan, P. (1997). Automatic text categorization using neural networks. In Proceedings of the 8th ASIS/SIGCR Workshop on Classification Research (pp. 59-72). Washington.
20. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1-47.
21. Sinka, M. P., & Corne, D. W. (2002). A large benchmark dataset for web document clustering. In A. Abraham, J. Ruiz-del-Solar, and M. Koeppen (eds.), Soft computing systems: design, management and applications (pp. 881-890). (Volume 87 of Frontiers in Artificial Intelligence and Applications, 2002).
22. Weiss, S. M., Apté, Damerau, F. J., Johnson, D. E., Oles, F. J., Goezt, T., & Hampp, T. (1999). Maximizing text-mining performance. IEEE Intelligent Systems, 14(4), 63-69.
23. Yang, Y., & Pedersen, J. P. (1997). A comparative study on feature selection in text categorization. In Proceedings of the Fourteenth International Conference on Machine Learning (pp. 412-420). Nashville.
24. Zechner, K. (1997). A literature survey on text summarization. Paper for Directed Reading (Fall 1996), Carnegie Mellon university. Computational Linguistics.