Rough set-based dimensionality reduction for supervised and unsupervised learning - Digital Library of Zielona Góra

Skip to main menu
Skip to search engine
Skip to content
Skip to footer

Title: Rough set-based dimensionality reduction for supervised and unsupervised learning

Creator:

Shen, Qiang ; Chouchoulas, Alexios

Date:

Resource Type:

Contributor:

Grzymala-Busse, Jerzy - ed. ; Świniarski, Roman W. - ed. ; Zhong, Ning - ed. ; Ziarko, Wojciech - ed.

Subtitle:

Rough Sets and Their Applications

Group publication title:

AMCS, volume 11 (2001)

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:58779

Pages:

Source:

AMCS, volume 11, number 3 (2001) ; click here to follow the link

Language:

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Subject and keywords:

knowledge-based systems fuzzy rule induction rough dimensionality reduction knowledge acquisition

Object collections:

Digital Library of Zielona Góra > Repository > Faculties > Faculty of Computer, Electrical and Control Engineering

Digital Library of Zielona Góra > Repository > Types of work > Articles

Digital Library of Zielona Góra > Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2001)

Last modified:

Sep 3, 2021

In our library since:

Jul 21, 2021

Number of object content hits:

42

All available object's versions:

https://www.zbc.uz.zgora.pl/publication/65562

Show description in RDF format:

Show description in OAI-PMH format:

International Journal of Applied Mathematics and Computer Science (AMCS)

Edition name	Date
Rough set-based dimensionality reduction for supervised and unsupervised learning	Sep 3, 2021

Objects

Similar

AMCS, Volume 21 (2011)

AMCS, Volume 21 (2011)

Engineering intelligent systems on the knowledge formalization continuum

Baumeister, Joachim Reutelshoefer, Jochen Puppe, Frank Korbicz, Józef - red. Uciński, Dariusz - red.

2011

artykuł

More

Contact details

Address

University Library
al. Wojska Polskiego 71
65-762 Zielona Góra

Phone

(+48) 68 328 21 55

E-Mail

kontakt@zbc.uz.zgora.pl

Cyprian Norwid Voivodeship and
City Public Library
al. Wojska Polskiego 9
65-077 Zielona Góra

(+48) 68 453 26 06

p.karp@biblioteka.zgora.pl

This page uses 'cookies'. More information