Abstract
Today computing devices generate abundant information which has to be classified and stored so that navigation becomes easier. Semi-supervised learning which is in-between supervised learning and unsupervised learning is explored, and fuzziness is incorporated in the process of textual classification. We apply semi-supervised classification where we have very less training data when compared to the supervised training. In addition to unlabeled data, the algorithm is provided with some supervision information but not for all example data. In addition traditional KNN takes similar weights to all the features in all classes, which is not reasonable. Based on the concept of variance, assigning different weights to the feature in different classes is explored resulting in enhancements to the traditional KNN algorithm.
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
Wajeed, M.A., Adilakshmi, T.: Text Classification Using Machine Learning. Journal of Theoretical and Applied Information Technology 7(2), 119–123 (2009)
Yen, J., Langari, R.: Fuzzy Logic-Intelligence, Control, and Information. Prentice- Hall (1999)
Wang, J.S., Lee, C.S.G.: Self-Adaptive Neurofuzzy Inference Systems for Classification Applications. IEEE Trans. Fuzzy Systems 10(6), 790–802 (2002)
Correa, R.F., Ludermir, T.B.: Automatic Text Categorization: Case Study. In: Proceedings of the VII Brazilian Symposium on Neural Networks, Pernambuc, Brazil (November 2002)
http://www.daviddlewis.com/resources/testcollections/reuters21578
Jiang, J.-Y., Liou, R.-J., Lee, S.-J.: Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification. IEEE Transaction on Knowledge & Data Engineering 23(3) (March 2011)
Wajeed, M.A., Adilakshmi, T.: Different Similarity Measures for Text Classification Using KNN. In: Proceedings of the International Conference on Computer Communication Technology at National Institute of Technology, Allahabad, September 15-17 (2011)
Sebastiani, F.: Text classification, automatic. In: Brown, K. (ed.) The Encyclopedia of Language and Linguistics, 2nd edn., vol. 14. Elsevier Science, Amsterdam (2004)
tartarus.org/Â martin/PorterStemmer
Yeung, C.-M.A., Gibbins, N., Shadbolt, N.: A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies, wi-iat. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 70–76 (2008)
Dalmau, M.C., Flórez, O.W.M.: Experimental Results of the Signal Processing Approach to Distributional Clustering of Terms on Reuters-21578 Collection. In: Proc. 29th European Conf. IR Research, pp. 678–681 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer India Pvt. Ltd.
About this paper
Cite this paper
Wajeed, M.A., Adilakshmi, T. (2012). Incorporating Fuzzy Clusters in Semi-supervised Text Categorization Using Enhanced KNN Algorithm. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_39
Download citation
DOI: https://doi.org/10.1007/978-81-322-0491-6_39
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0490-9
Online ISBN: 978-81-322-0491-6
eBook Packages: EngineeringEngineering (R0)