Abstract
The manipulation of large-scale document data sets often involves the processing of a wealth of features that correspond with the available terms in the document space. The employment of all these features in the learning machine of interest is time consuming and at times reduces the performance of the learning machine. The feature space may consist of many redundant or non-discriminant features; therefore, feature selection techniques have been widely used. In this paper, we introduce a hybrid feature selection algorithm that selects features by applying both filter and wrapper methods in a hybrid manner, and iteratively selects the most competent set of features with an expectation maximization based algorithm. The proposed method employs a greedy algorithm for feature selection in each step. The method has been tested on various data sets whose results have been reported in this paper. The performance of the method both in terms of accuracy and Normalized Mutual Information is promising.
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.
Similar content being viewed by others
References
Breiman, L.: Classification and Regression Trees. Chapman & Hall/CRC, Boca Raton (1998)
Chua, S., Kulathuramaiyer, N.: Semantic feature selection using wordnet. In: WI 2004: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Washington, DC, USA, pp. 166–172. IEEE Computer Society, Los Alamitos (2004)
Dash, M., Choi, K., Scheuermann, P., Liu, H.: Feature selection for clustering - a filter solution. In: ICDM, pp. 115–122 (2002)
Dhillon, I., Kogan, J., Nicholas, C.: Feature Selection and Document Clustering, Survey of Text Mining: Clustering, Classification, and Retrieval (2004)
Forman, G.: An extensive empirical study of feature selection metrics for text classification. The Journal of Machine Learning Research 3, 1289–1305 (2003)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003)
sam Han, E.h., Boley, D., Gini, M., Gross, R., Hastings, K., Karypis, G., Kumar, V., Mobasher, B., Moore, J.: Webace: a web agent for document categorization and exploration. In: Proc. of the 2nd International Conference on Autonomous Agents, pp. 408–415. ACM Press, New York (1998)
Jain, A., Zongker, D.: Feature selection: evaluation, application, and small sampleperformance. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(2), 153–158 (1997)
Jolliffe, I.T.: Principal component analysis. Springer, New York (2002)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)
Liu, T., Liu, S., Chen, Z., Ma, W.-Y.: An evaluation on feature selection for text clustering. In: ICML, pp. 488–495 (2003)
Strehl, A., Ghosh, J.: Cluster Ensembles-A Knowledge Reuse Framework for Combining Partitionings. In: Proceedings of the National Conference on Artificial Intelligence, pp. 93–99. AAAI Press, MIT Press, Menlo Park, Cambridge (1999) (2002)
Wolf, L., Shashua, A.: Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weight-based approach. J. Mach. Learn. Res. 6, 1855–1887 (2005)
Yang, Y.: Noise reduction in a statistical approach to text categorization. In: Proceedings of the 18th Ann Int ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1995), pp. 256–263. ACM Press, New York (1995)
Zhao, Z., Liu, H.: Spectral feature selection for supervised and unsupervised learning. In: ICML, pp. 1151–1157 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jashki, MA., Makki, M., Bagheri, E., Ghorbani, A.A. (2009). An Iterative Hybrid Filter-Wrapper Approach to Feature Selection for Document Clustering. In: Gao, Y., Japkowicz, N. (eds) Advances in Artificial Intelligence. Canadian AI 2009. Lecture Notes in Computer Science(), vol 5549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01818-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-01818-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01817-6
Online ISBN: 978-3-642-01818-3
eBook Packages: Computer ScienceComputer Science (R0)