Rough set-based dimensionality reduction for supervised and unsupervised learning

Qiang Shen; Alexios Chouchoulas

International Journal of Applied Mathematics and Computer Science (2001)

  • Volume: 11, Issue: 3, page 583-601
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Shen, Qiang, and Chouchoulas, Alexios. "Rough set-based dimensionality reduction for supervised and unsupervised learning." International Journal of Applied Mathematics and Computer Science 11.3 (2001): 583-601. <http://eudml.org/doc/207521>.

@article{Shen2001,
abstract = {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.},
author = {Shen, Qiang, Chouchoulas, Alexios},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {fuzzy rule induction; knowledge acquisition; knowledge-based systems; rough dimensionality reduction; machine learning},
language = {eng},
number = {3},
pages = {583-601},
title = {Rough set-based dimensionality reduction for supervised and unsupervised learning},
url = {http://eudml.org/doc/207521},
volume = {11},
year = {2001},
}

TY - JOUR
AU - Shen, Qiang
AU - Chouchoulas, Alexios
TI - Rough set-based dimensionality reduction for supervised and unsupervised learning
JO - International Journal of Applied Mathematics and Computer Science
PY - 2001
VL - 11
IS - 3
SP - 583
EP - 601
AB - 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.
LA - eng
KW - fuzzy rule induction; knowledge acquisition; knowledge-based systems; rough dimensionality reduction; machine learning
UR - http://eudml.org/doc/207521
ER -

References

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  13. Ripley B.D. (1996): Pattern Recognition and Neural Networks. - Cambridge: Cambridge University Press. Zbl0853.62046
  14. 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. 
  15. 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. 
  16. 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. Zbl0397.68071

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