Abstract
Nowadays, automated Web document classification is considered as an important method to manage and process an enormous amount of Web documents in digital forms that are extensive and constantly increasing. Recently, document classification has been addressed with various classified techniques such as naïve Bayesian, TFIDF (Term Frequency Inverse Document Frequency), FCA (Formal Concept Analysis) and MCRDR (Multiple Classification Ripple Down Rules). We suggest the BayesTH-MCRDR algorithm for useful new Web document classification in this paper. We offer a composite algorithm that combines a naïve Bayesian algorithm using Threshold and the MCRDR algorithm. The prominent feature of the BayesTH-MCRDR algorithm is optimisation of the initial relationship between keywords before final assignment to a category in order to get higher document classification accuracy. We also present the system we have developed in order to demonstrate and compare a number of classification techniques.
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
Mitchell, T.: Machine Learning. International Edition. McGraw-Hill, New York (1995)
Joachims, T.: A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In: Proceedings of the 14th International Conference on Machine Learning (ICML 1997), pp. 143–151 (1997)
Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Ordered sets, pp. 445–470 (1982)
Kang, B.H.: Validating Knowledge Acquisition: Multiple Classification Ripple Down Rules, PhD dissertation, School of Computer Science and Engineering at the University of New South Wales (1995)
McCallum, A., Nigram, K.: A Comparison of Event Models for Naïve Bayes Text Classification. In: AAAI 1998 Workshop on Learning for Tex Categorization (1998)
Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the 14th International Conference on Machine Learning, pp. 412–420 (1997)
Birkhoff, G.: Lattice Theory 3rdedition, American Mathematical Society, Incremental Clustering for Dynamic Information Processing. ACM Transactions on Information Processing Systems 11, 143–164 (1993)
Ganter, B., Wille, R.: General lattice theory, 2nd edn., pp. 591–605. Birkhauser, Basel (1998)
Ganter, B., Wille, R.: Formal Concept Analysis – mathematical Foundations Berlin. Springer, Heidelberg (1999)
Lewis, D.D.: Feature Selection and Feature Extraction for Text Categorization. In: Proceedings of Speech and Natural Language Workshop, pp. 212–217 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cho, WC., Richards, D. (2004). BayesTH-MCRDR Algorithm for Automatic Classification of Web Document. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_31
Download citation
DOI: https://doi.org/10.1007/978-3-540-30549-1_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24059-4
Online ISBN: 978-3-540-30549-1
eBook Packages: Computer ScienceComputer Science (R0)