Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/3790
Title: A framework of fuzzy variable precision rough sets and its applications
Authors: Zhao, Suyun
Keywords: Hong Kong Polytechnic University -- Dissertations
Rough sets
Fuzzy sets
Machine learning
Issue Date: 2009
Publisher: The Hong Kong Polytechnic University
Abstract: The traditional rough sets model was restrictive for many applications since it could only handle the symbolic valued databases without misclassification. Here misclassification means the wrong or missing information in classification. As a generalization, the Variable Precision Rough Set model (VPRS) was introduced to handle databases with misclassification. However it could not effectively handle numerical data (real number data). To handle the databases with non-symbolic values, some generalization models of rough sets, called Fuzzy Rough Sets (FRS), have been introduced by combining fuzzy sets and rough sets. It could handle databases with numerical data. However, FRS was sensitive to misclassification and perturbation. Here perturbation means small change between the observed data and factual data. It is valuable to combine FRS and VPRS so that a powerful tool, which can effectively handle numerical data with misclassification and perturbation, is developed. In this thesis we first study two applications of FRS and find their limitations in real worlds which show the necessity to propose a robust model. Second, we generalize FRS and then propose a robust model named Fuzzy Variable Precision Rough Sets (FVPRS). After compring FVPRS with some flexible generalizations of RS, we find that FVPRS has good prospects for some real applications. Third, we establish two applications with FVPRS: attribute reduction and classifier construction. In these two applications, we propose the concepts of reductions in FVPRS and investigate their structures by using discernibility matrix (or vector). We also develop the algorithms to find the reductions. Thus a unified variable precision framework of FRS is set up. Finally in order to overcome NP-hard problem of finding the optimal solutions, we design some fast heuristic algorithms (the computational complexities of these algorithms increase with the square of the number of attributes) to obtain near-optimal solutions. From the theoretical viewpoints, the main contribution of this thesis is that we propose a robust framework with respect to misclassification and perturbation. From the practical viewpoint, the main contribution of this thesis is that two robust applications of FVPRS are proposed based on the strict mathematical reasoning.
Description: xiii, 160 p. : ill. ; 30 cm.
PolyU Library Call No.: [THS] LG51 .H577P COMP 2009 Zhao
URI: http://hdl.handle.net/10397/3790
Rights: All rights reserved.
Appears in Collections:Thesis

Files in This Item:
File Description SizeFormat 
b23068061_link.htmFor PolyU Users 162 BHTMLView/Open
b23068061_ir.pdfFor All Users (Non-printable) 2.99 MBAdobe PDFView/Open
Show full item record

Page view(s)

474
Last Week
2
Last month
Checked on Mar 26, 2017

Download(s)

418
Checked on Mar 26, 2017

Google ScholarTM

Check



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.