Interval-valued Fuzzy-Rough Feature Selection and Application for Handling Missing Values in Datasets

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Abstract

One of the many successful applications of rough set theory has been to the area of feature selection. The rough set ideology of using only the supplied data and no other information has many benefits, where most other methods require supplementary knowledge. Fuzzy-rough set theory has recently been proposed as an extension of this, in order to better handle the uncertainty present in real data. However, following this approach, there has been no investigation (theoretical or otherwise) into how to deal with missing values effectively, another problem encountered when using real world data. This paper proposes an extension of the fuzzy-rough feature selection methodology, based on interval-valued fuzzy sets, as a means to counter this problem via the representation of missing values in an intuitive way.
Original languageEnglish
Title of host publication8th Annual UK Workshop on Computational Intelligence (UKCI'08)
Pages59-64
Number of pages6
Publication statusPublished - 2008
Event8th Annual UK Workshop on Computational Intelligence (UKCI'08) - Leicester, United Kingdom of Great Britain and Northern Ireland
Duration: 10 Sept 200812 Sept 2008

Conference

Conference8th Annual UK Workshop on Computational Intelligence (UKCI'08)
Country/TerritoryUnited Kingdom of Great Britain and Northern Ireland
CityLeicester
Period10 Sept 200812 Sept 2008

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