Boosting based fuzzy-rough pattern classifier

Prahlad Vadakkepat, P. Pramod Kumar, Sivakumar Ganesan, Loh Ai Poh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationTrends in Intelligent Robotics - 13th FIRA Robot World Congress, FIRA 2010, Proceedings
Number of pages8
StatePublished - 2010
Event13th FIRA Robot World Congress on Trends in Intelligent Robotics, FIRA 2010 - Bangalore, India
Duration: Sep 15 2010Sep 17 2010

Publication series

NameCommunications in Computer and Information Science
Volume103 CCIS
ISSN (Print)1865-0929


Other13th FIRA Robot World Congress on Trends in Intelligent Robotics, FIRA 2010


Dive into the research topics of 'Boosting based fuzzy-rough pattern classifier'. Together they form a unique fingerprint.

Cite this