Simultaneous feature and instance selection using fuzzy-rough bireducts

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

19 Citations (SciVal)
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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.
Ever since its inception, this theory has been successfully
employed for developing computationally efficient techniques for
addressing problems such as the discovery of hidden patterns
in data, decision rule induction, and feature selection. As an
extension to this theory, fuzzy-rough sets enhance the ability to
model uncertainty and vagueness more effectively. The efficacy of
fuzzy-rough set based approaches for the tasks of feature selection
and rule induction is now well established in the literature.
Although some work has been carried out using fuzzy-rough set
theory for the tasks of feature selection and instance selection in
isolation, the potential of this theory for its application to tasks for
the simultaneous selection of both features and instances has not
been investigated thus far. This paper proposes a novel method
for simultaneous instance and feature selection based on fuzzyrough
sets. The initial experimentation demonstrates that the
method can significantly reduce both the number of instances
and features whilst maintaining high classification accuracies.
Original languageEnglish
Title of host publicationProceedings of the 22nd IEEE International Conference on Fuzzy Systems
PublisherIEEE Press
Publication statusPublished - 07 Jul 2013


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