Feature Grouping-Based Fuzzy-Rough Feature Selection

Richard Jensen*, Neil MacParthalain, Chris Cornelis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

11 Citations (Scopus)

Abstract

Data dimensionality has become a pervasive problem in many areas that require the learning of interpretable models. This has become particularly pronounced in recent years with the seemingly relentless growth in the size of datasets. Indeed, as the number of dimensions increases, the number of data instances required in order to generate accurate models increases exponentially. Feature selection has therefore become not only a useful step in the process of model learning, but rather an increasingly necessary one. Rough set and fuzzy-rough set theory have been used as such dataset pre-processors with much success, however the underlying time/space complexity of the subset evaluation metric is an obstacle to the processing of very large data. This paper proposes a general approach to this problem that employs a novel feature grouping step in order to alleviate the processing overhead for large datasets. The approach is framed within the context of (and applied to) fuzzy-rough sets, although it can be used with other subset evaluation techniques. The experimental evaluation demonstrates that considerable computational effort can be avoided, and as a result efficiency can be improved considerably for larger datasets.

Original languageEnglish
Title of host publication2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
Place of PublicationNEW YORK
PublisherIEEE Press
Pages1488-1495
Number of pages8
Publication statusPublished - 2014
EventFuzzy Systems - Beijing, Beijing, China
Duration: 06 Jul 201411 Jul 2014
Conference number: 23

Publication series

NameIEEE International Fuzzy Systems Conference Proceedings
PublisherIEEE
ISSN (Print)1544-5615

Conference

ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2014
Country/TerritoryChina
CityBeijing
Period06 Jul 201411 Jul 2014

Keywords

  • fuzzy-rough sets
  • feature selection
  • feature grouping

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