Result: Forward approximation and backward approximation in fuzzy rough sets

Title:
Forward approximation and backward approximation in fuzzy rough sets
Authors:
YI CHENG
Source:
Neurocomputing (Amsterdam). 148:340-353
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 45 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Acquisition connaissances, Knowledge acquisition, Adquisición de conocimientos, Analyse donnée, Data analysis, Análisis datos, Classification, Clasificación, Critère sélection, Selection criterion, Criterio selección, Discernabilité, Discernability, Discernabilidad, Ensemble flou, Fuzzy set, Conjunto difuso, Extraction connaissances, Knowledge extraction, Extracción conocimiento, Fouille donnée, Data mining, Busca dato, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Logique floue, Fuzzy logic, Lógica difusa, Méthode réduction, Reduction method, Método reducción, Réduction dimension, Dimension reduction, Reducción dimensión, Réduction donnée, Data reduction, Reducción datos, Réduction système, System reduction, Reducción sistema, Résultat expérimental, Experimental result, Resultado experimental, Système expert, Expert system, Sistema experto, Système information, Information system, Sistema información, Théorie ensemble approximatif, Rough set theory, Teoría de los Conjuntos Aproximados, Fuzzy rough sets, Rule extraction
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Information Engineering, Sichuan College of Architectural Technology, Deyang 618000, China
ISSN:
0925-2312
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28844549
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.28844549
Database:
PASCAL Archive

Further Information

It is general to obtain rules by attribute reduction in fuzzy information systems. Instead of obtaining rules by attribute reduction, which may have a negative effect on inducting good rules, the objective of this paper is to extract rules without computing attribute reducts. Forward and backward approximations in fuzzy rough sets are first defined, and their important properties are discussed. Two algorithms based on forward and backward approximations, namely, mine rules based on the forward approxima- tion (MRBFA) and mine rules based on the backward approximation (MRBBA), are proposed for rule extraction. The two algorithms are evaluated by several data sets from the UC Irvine Machine Learning Repository. The experimental results show that both MRBFA and MRBBA achieve better classification performances than the method based on attribute reduction.