Creator:
Contributor:
Grzymala-Busse, Jerzy - ed. ; Świniarski, Roman W. - ed. ; Zhong, Ning - ed. ; Ziarko, Wojciech - ed.
Title:
Rough sets methods in feature reduction and classification
Subtitle:
Rough Sets and Their Applications
Group publication title:
Subject and Keywords:
rough sets ; feature selection ; classification
Abstract:
The paper presents an application of rough sets and statistical methods to feature reduction and pattern recognition. The presented description of rough sets theory emphasizes the role of rough sets reducts in feature selection and data reduction in pattern recognition. The overview of methods of feature selection emphasizes feature selection criteria, including rough set-based methods. ; The paper also contains a description of the algorithm for feature selection and reduction based on the rough sets method proposed jointly with Principal Component Analysis. Finally, the paper presents numerical results of face recognition experiments using the learning vector quantization neural network, with feature selection based on the proposed principal components analysis and rough sets methods.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Date:
Resource Type:
Pages:
Source:
AMCS, volume 11, number 3 (2001) ; click here to follow the link