Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-15810-0_39
Title: Boosting based fuzzy-rough pattern classifier
Authors: Vadakkepat, P. 
Pramod Kumar, P.
Ganesan, S.
Poh, L.A. 
Issue Date: 2010
Citation: Vadakkepat, P.,Pramod Kumar, P.,Ganesan, S.,Poh, L.A. (2010). Boosting based fuzzy-rough pattern classifier. Communications in Computer and Information Science 103 CCIS : 306-313. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-15810-0_39
Abstract: A novel classification algorithm based on the rough set concepts of fuzzy lower and upper approximations is proposed. The algorithm transforms each quantitative value of a feature into fuzzy sets of linguistic terms using membership functions and calculates the fuzzy lower and upper approximations. The membership functions are generated from cluster points generated by the subtractive clustering technique. A certain rule set based on fuzzy lower approximation and a possible rule set based on fuzzy upper approximation are generated. A genetic algorithm, based on iterative rule learning in combination with a boosting technique, is used to generate the possible rules. The proposed classifier is tested with three well known datasets from the UCI machine learning repository, and compared with relevant classification methods. © 2010 Springer-Verlag.
Source Title: Communications in Computer and Information Science
URI: http://scholarbank.nus.edu.sg/handle/10635/69529
ISBN: 3642158099
ISSN: 18650929
DOI: 10.1007/978-3-642-15810-0_39
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

3
checked on Feb 19, 2019

Page view(s)

47
checked on Jan 12, 2019

Google ScholarTM

Check

Altmetric


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