Abstract
Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, solution to this has found successful application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classification. Rough set theory has been used as such a dataset pre-processor with much success, but current methods are inadequate at finding minimal reductions, the smallest sets of features possible. This paper proposes a technique that considers this problem from a propositional satisfiability perspective. In this framework, minimal subsets can be located and verified. An initial experimental investigation is conducted, comparing the new method with a standard rough set-based feature selector.
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References
Bakar, A.A., Sulaiman, M.N., Othman, M., Selamat, M.H.: IP algorithms in compact rough classification modeling. Intelligent Data Analysis 5(5), 419–429 (2001)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. Irvine, University of California (1998), http://www.ics.uci.edu/~mlearn/
Chouchoulas, A., Shen, Q.: Rough set-aided keyword reduction for text categorisation. Applied Artificial Intelligence 15(9), 843–873 (2001)
Dash, M., Liu, H.: Feature Selection for Classification. Intelligent Data Analysis 1(3), 131–156 (1997)
Davis, M., Logemann, G., Loveland, D.: A machine program for theorem proving. Communications of the ACM 5, 394–397 (1962)
Hoos, H.H., Stützle, T.: Towards a Characterisation of the Behaviour of Stochastic Local Search Algorithms for SAT. Artificial Intelligence 112, 213–232 (1999)
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)
Kryszkiewicz, M.: Comparative Study of Alternative Types of Knowledge Reduction in Inconsistent Systems. International Journal of Intelligent Systems 16(1), 105–120 (2001)
Nguyen, H.S., Skowron, A.: Boolean Reasoning for Feature Extraction Problems. In: Proceedings of the 10th International Symposium on Methodologies for Intelligent Systems, pp. 117–126 (1997)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991)
Polkowski, L.: Rough Sets: Mathematical Foundations. Advances in Soft Computing. Physica, Heidelberg (2002)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in Information Systems. In: Slowinski, R. (ed.) Intelligent Decision Support, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)
Starzyk, J.A., Nelson, D.E., Sturtz, K.: Reduct Generation in Information Systems. Bulletin of the International Rough Set Society 3(1-2), 19–22 (1999)
Zhang, L., Malik, S.: The Quest for Efficient Boolean Satisfiability Solvers. In: Voronkov, A. (ed.) CADE 2002. LNCS (LNAI), vol. 2392, pp. 295–313. Springer, Heidelberg (2002)
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Jensen, R., Shen, Q., Tuson, A. (2005). Finding Rough Set Reducts with SAT. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_21
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DOI: https://doi.org/10.1007/11548669_21
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