Abstract
Text classification techniques mostly rely on single term analysis of the document data set, while more concepts especially the specific ones are usually conveyed by set of terms. To achieve more accurate text classifier, more informative feature including frequent co-occurring words in the same sentence and their weights are particularly important in such scenarios. In this paper, we propose a novel approach using sentential frequent itemset, a concept comes from association rule mining, for text classification, which views a sentence rather than a document as a transaction, and uses a variable precision rough set based method to evaluate each sentential frequent itemset’s contribution to the classification. Experiments over the Reuters corpus are carried out, which validate the practicability of the proposed system.
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
Li, W., Pei, J.: CMAR.: Accurate and efficient classification based on multiple class-association rules. In: IEEE international Conference on Data Mining (ICDM 2001), San Jose, California, Novermber 29-December 2 (2001)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: ACM Int. Conf. on Knowledge Discovery and Data Mining (SIGKDD 1998), New York City, NY, August 1998, pp. 80–86 (1998)
Antonie, M., Zaiane, O.R.: Text Document Categorization by Term Assosiation. In: Proc. of IEEE Intl. Conf. on Data Mining, pp. 19–26 (2002)
Meretakis, D., Fragoutids, D., Lu, H., Likothanasis, S.: Scalable Association-based Text Classification. In: Proc. of ACM CIKM (2000)
Poter, M.F.: An Algorithm for Suffix Stripping. Program 14(3), 130–137 (1980)
Salton, G., Wong, A., Yang, C.: A Vector Space Model for Automatic Indexing. Comn. ACM 18(11), 613–620 (1975)
Salton, G.: Automatic Text Processing: The Transformation, Analysis and Retrieval of Information by computer. Addison Wesley, Reading (1989)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Hull, D.A.: Improving text retrieval for the routing problem using latent semantic indexing. In: 17th ACM international Conference on Machine learning (ECML 1998), pp. 137–142 (1998)
Lewis, D.: Naïve (bayes) at forty: The independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 4–15. Springer, Heidelberg (1998)
Cohen, W., Hirsch, H.: Joins that generalize: text classification using whirl. In: 4th International Conference on Knowledge Discovery and Data Mining (SigKDD 1998), New York City, USA, pp. 169–173 (1998)
Cohen, W., Singer, Y.: Context-sensitive learning methods for text categorization. ACM transaction on Information systems 17(2), 146–173 (1999)
Joachims, T.: Text categorization with support vector machines: learning with many relevant feature. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Yang, Y.: An evaluation of statistical approaches to text categorization. Technical Report CUM-CS-97-127, Carnegie mellon University (April 1997)
Tan, C.M., Wang, Y.F., Lee, C.D.: The use of bigrams to enhance text categorization. Journal of Information Processing and Management (2002)
Ruiz, M., Sinivasan, P.: Neural networks for text categorization. In: 22nd ACM SIGIR international Conference on Information Retrieval, Berkeley, CA, USA, August 1999, pp. 282–282 (1999)
Yang, Y., Liu, X.: A re-examination of text categorization methods. In: 22nd ACM international Conference on Research and Development in Information Retrieval (SIGIR 1999), Berkeley, US, pp. 42–49 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, S., Hu, H. (2005). A Hybrid Text Classification System Using Sentential Frequent Itemsets. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_65
Download citation
DOI: https://doi.org/10.1007/11596448_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
Online ISBN: 978-3-540-31599-5
eBook Packages: Computer ScienceComputer Science (R0)