Heuristic search for fuzzy-rough bireducts and its use in classifier ensembles

Type Conference Proceeding (Non-Journal item)
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
PublisherIEEE Press
Pages1504-1511
Number of pages8
ISBN (Print)978-1479920723
DOI
Publication statusPublished - 01 Jan 2014
Event2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing, United Kingdom of Great Britain and Northern Ireland
Duration: 06 Jul 201411 Jul 2014

Conference

Conference2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014
CountryUnited Kingdom of Great Britain and Northern Ireland
CityBeijing
Period06 Jul 201411 Jul 2014
Links
Handle.net
View graph of relations
Citation formats

Abstract

Rough set theory has proven to be a useful mathematical basis for developing automated computational approaches which are able to deal with and utilise imperfect knowledge. Fuzzy-rough set theory is an extension to rough set theory and enhances the ability to model uncertainty and vagueness more effectively. There have been many developments in this area which offer robust methods for feature selection or instance selection. However, these are often carried out in isolation rather than considering both types of selection simultaneously. For this purpose, the notion of a bireduct has been proposed recently but the task of finding bireducts of high quality remains a significant challenge. This paper presents a heuristic strategy for the identification of fuzzy-rough bireducts, which is based on a music-inspired global optimisation algorithm called harmony search. The concept of e-bireducts is employed in this approach for the evaluation and improvisation of the candidate solutions. The stochastically-selected bireducts are also utilised to construct classifier ensembles. The presented technique is experimentally evaluated using a number of real-valued benchmark data sets.