Abstract
Due to the explosive growth of electronically stored information, automatic methods must be developed to aid users in maintaining and using this abundance of information effectively. In particular, the sheer volume of redundancy present must be dealt with, leaving only the information-rich data to be processed. This paper presents an approach, based on an integrated use of fuzzy-rough sets and Ant Colony Optimization (ACO), to greatly reduce this data redundancy. The work is applied to the problem of webpage categorization, considerably reducing dimensionality with minimal loss of information.
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References
Bonabeau, E., Dorigo, M., Theraulez, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press Inc., New York (1999)
Chouchoulas, A., Shen, Q.: Rough set-aided keyword reduction for text categorisation. Applied Artificial Intelligence 15(9), 843–873 (2001)
Cohen, W.W.: Fast effective rule induction. In: Machine Learning: Proceedings of the 12th International Conference, pp. 115–123 (1995)
Dash, M., Liu, H.: Feature Selection for Classification. Intelligent Data Analysis 1(3), 131–156 (1997)
Dubois, D., Prade, H.: Putting rough sets and fuzzy sets together. In: Slowinski, R. (ed.) Intelligent Decision Support, pp. 203–232. Kluwer Academic Publishers, Dordrecht (1992)
Han, J., Hu, X., Lin, T.Y.: Feature Subset Selection Based on Relative Dependency between Attributes. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS, vol. 3066, pp. 176–185. Springer, Heidelberg (2004)
Jensen, R., Shen, Q.: Fuzzy-rough attribute reduction with application to web categorization. Fuzzy Sets and Systems 141(3), 469–485 (2004)
Jensen, R., Shen, Q.: Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches. IEEE Transactions on Knowledge and Data Engineering 16(12), 1457–1471 (2004)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991)
Quinlan, J.R.: C4.5: Programs for Machine Learning. The Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
Rasmani, K., Shen, Q.: Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers. In: Proceedings of the 13th International Conference on Fuzzy Systems, pp. 1687–1694 (2004)
Salton, G., Buckley, C.: Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management 24(5), 513–523 (1988)
Witten, I.H., Frank, E.: Generating Accurate Rule Sets Without Global Optimization. In: Machine Learning: Proceedings of the 15th International Conference. Morgan Kaufmann Publishers, San Francisco (1998)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann Publishers, San Francisco (2000)
Yahoo, http://www.yahoo.com
Yao, J., Zhang, M.: Feature Selection with Adjustable Criteria. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS, vol. 3641, pp. 204–213. Springer, Heidelberg (2005)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
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Jensen, R., Shen, Q. (2006). Webpage Classification with ACO-Enhanced Fuzzy-Rough Feature Selection. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_17
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DOI: https://doi.org/10.1007/11908029_17
Publisher Name: Springer, Berlin, Heidelberg
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