# 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

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topShen, 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 -

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