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2001 | 11 | 3 | 583-601
Tytuł artykułu

Rough set-based dimensionality reduction for supervised and unsupervised learning

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The curse of dimensionality is a damning factor for numerous potentially powerful machine learning techniques. Widely approved and otherwise elegant methodologies used for a number of different tasks ranging from classification to function approximation exhibit relatively high computational complexity with respect to dimensionality. This limits severely the applicability of such techniques to real world problems. Rough set theory is a formal methodology that can be employed to reduce the dimensionality of datasets as a preprocessing step to training a learning system on the data. This paper investigates the utility of the Rough Set Attribute Reduction (RSAR) technique to both supervised and unsupervised learning in an effort to probe RSAR's generality. FuREAP, a Fuzzy-Rough Estimator of Algae Populations, which is an existing integration of RSAR and a fuzzy Rule Induction Algorithm (RIA), is used as an example of a supervised learning system with dimensionality reduction capabilities. A similar framework integrating the Multivariate Adaptive Regression Splines (MARS) approach and RSAR is taken to represent unsupervised learning systems. The paper describes the three techniques in question, discusses how RSAR can be employed with a supervised or an unsupervised system, and uses experimental results to draw conclusions on the relative success of the two integration efforts.
Rocznik
Tom
11
Numer
3
Strony
583-601
Opis fizyczny
Daty
wydano
2001
otrzymano
2001-03-01
poprawiono
2001-06-01
Twórcy
autor
  • Institute for Representation and Reasoning, Division of Informatics, The University of Edinburgh, Edinburgh EH1 1HN, U.K
  • Institute for Representation and Reasoning, Division of Informatics, The University of Edinburgh, Edinburgh EH1 1HN, U.K
Bibliografia
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  • ERUDIT, European Network for Fuzzy Logic and Uncertainty Modeling in Information Technology. Protecting Rivers and Streams by Monitoring Chemical Concentrations and Algae Communities (Third International Competition) http://www.erudit.de/erudit/activities/ic-99/problem.htm
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  • Jelonek J., Krawiec K. and Slowinski R. (1995): Rough set reduction of attributes and their domains for neural networks. - Comput. Intell., Vol.11, No.2, pp.339-347.
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  • Mitchell T.M. (1997): Machine Learning. - New York: McGraw-Hill.
  • Pawlak Z. (1991): Rough Sets: Theoretical Aspects of Reasoning About Data. - Dordrecht: Kluwer.
  • Quinlan J.R. (1993): C4.5: Programs for Machine Learning. - San Mateo: Morgan Kaufmann.
  • van Rijsbergen C.J. (1979): Information Retrieval. - London: Butterworths.
  • Ripley B.D. (1996): Pattern Recognition and Neural Networks. - Cambridge: Cambridge University Press.
  • Shen Q. and Chouchoulas A. (1999): Data-driven fuzzy rule induction and its application to systems monitoring. - Proc. 8-th IEEE Int. Conf. Fuzzy Systems, Seoul, Korea, Vol.2, pp.928-933.
  • Shen Q. and Chouchoulas A. (2000): A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems. - Eng. Appl. Artif. Intell., Vol.13, No.3, pp.263-278.
  • Zadeh L. (1975): The concept of a linguistic variable and its application to approximate reasoning - I. - Inform. Sci., Vol.8, No.1, pp.199-249.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv11i3p583bwm
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