Effective instance selection using the fuzzy-rough lower approximation

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

1 Citation (Scopus)


Fuzzy-rough set theory has been applied with much success to the problem of feature selection, where there is a clear link between the constructs (i.e., lower approximation, positive region, etc) and the problem (i.e., finding reducts). However, there has not been much development with regards to instance selection. Previous techniques have focused on preserving the positive region or the dependency, or have concentrated on prototype selection. This paper proposes a general instance selection approach that is efficient and effective in finding reductions that maintain the integrity of the underlying class structure. By utilizing the lower approximation information, instances can be removed that have a high similarity with more representative instances of a class. In addition to a threshold-based approach, a fully automated method that requires no user input is also presented. The experimentation demonstrates that the proposed method is indeed effective at reducing the number of instances with minimal information loss.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Fuzzy Systems
Subtitle of host publicationFUZZ-IEEE
PublisherIEEE Press
ISBN (Electronic)9781538617281
ISBN (Print)9781538617298
Publication statusPublished - 10 Oct 2019
EventFuzzy Systems - J W Marriott, New Orleans, United States of America
Duration: 23 Jun 201926 Jun 2019
Conference number: 28

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584
ISSN (Electronic)1558-4739


ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2019
Country/TerritoryUnited States of America
CityNew Orleans
Period23 Jun 201926 Jun 2019


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