Abstract
This paper investigates the effect of a cascaded feature selection (CFS) in SVMs text categorization. Unlike existing feature selections, our method (CFS) has two advantages. One can make use of the characteristic of each feature (word). Another is that unnecessary test documents for a category, which should be categorized into a negative set, can be removed in the first step. Compared with the method which does not apply CFS, our method achieved good performance especially about the categories which contain a small number of training documents.
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
Fukumoto, F. and Suzuki, Y.: Learning Lexical Representation for Text Categorization. Proceedings of the NAACL’01 Workshop on Wordnet and Other Lexical Resources: Applications, Extensions and Customizations, pp.156–161 (2001).
Taira, H. and Haruno, M.: Feature Selection in SVM Text Categorization. Proceedings of the 16th National Conference on Artificial Intelligence (AAAI’99), pp.480–486 (1999).
Takamura, H. and Matsumoto, Y.: Feature Space Restructuring for SVMs with Application to Text Categorization. Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing (EMNLP’01), pp.51–57 (2001).
Yang, Y.: A Study on Thresholding Strategies for Text Categorization. Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’01), pp.137–145 (2001).
Yang, Y. and Liu, X.: A re-examination of text categorization methods. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’99), pp.42–49 (1999).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Masuyama, T., Nakagawa, H. (2003). Cascaded Feature Selection in SVMs Text Categorization. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2003. Lecture Notes in Computer Science, vol 2588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36456-0_65
Download citation
DOI: https://doi.org/10.1007/3-540-36456-0_65
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00532-2
Online ISBN: 978-3-540-36456-6
eBook Packages: Springer Book Archive