Abstract
This work presents an application example of text document filtering. We compare the DIMLP neural hybrid model to several machine learning algorithms. The clear advantage of this neural hybrid system is its transparency. In fact, the classification strategy of DIMLPs is almost completely encoded into the extracted rules. During cross-validation trials and in the majority of the situations, DIMLPs demonstrated to be at least as accurate as support vector machines, which is one of the most accurate classifiers of the text categorization domain. In the future, in order to further increase DIMLP accuracy, we believe that common sense knowledge could be easily inserted and refined with the use of symbolic rules.
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
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based Learning Algorithms. Machine Learning 6(1), 37–66 (1991)
Bologna, G.: Is it Worth Generating Rules from Neural Network Ensembles? Journal of Applied Logic 2(3), 325–348 (2004)
Dumais, S.T., Platt, J., Heckerman, D., Sahami, M.: Inductive Learning Algorithms and Representations for Text Categorization. In: Proc. of CIKM-98, 7th ACM Int. Conf. on Information and Knowledge Management, pp. 148–155 (1998)
Miller, G.A.: WordNet: a Lexical Database for English. Commun. ACM 38(11) (1995)
Quinlan, J.R.: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Comput. Surv. 34(1), 1–47 (2002)
Tzeras, K., Hartmann, S.: Automatic Indexing Based on Bayesian Inference Networks. In: Proc. of SIGIR-93, 16th ACM Int. Conf. on Research and Development in Information Retrieval, pp. 22–34 (1993)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proc. of ICML-97, 14th Int. Conf. on Machine Learning, pp. 412–420 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Bologna, G., Boretti, M., Albuquerque, P. (2007). Filtering Documents with a Hybrid Neural Network Model. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-73053-8_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73052-1
Online ISBN: 978-3-540-73053-8
eBook Packages: Computer ScienceComputer Science (R0)