Nearest neighbour-based fuzzy-rough feature selection

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

5 Citations (SciVal)

Abstract

Research in the area of fuzzy-rough set theory and its application to various areas of learning have generated great interest in recent years. In particular, there has been much work in the area of feature or attribute selection. Indeed, as the number of dimensions increases, the number of data objects required in order to generate accurate models increases exponentially. Thus, feature selection (FS) has become an increasingly necessary step in model learning. The use of fuzzy-rough sets as dataset pre-processors offers much in the way of flexibility, however the underlying complexity of the subset evaluation metric often presents a problem and can result in a great deal of potentially unnecessary computational effort. This paper proposes two different novel ways to address this problem using a neighbourhood approximation step in order to alleviate the processing overhead and reduce the complexity of the evaluation metric. The experimental evaluation demonstrates that much computational effort can be avoided, and as a result the efficiency of the FS process can be improved considerably.

Original languageEnglish
Title of host publicationRough Sets and Current Trends in Soft Computing
Subtitle of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsChris Cornelis, Marzena Kryszkiewicz, Dominik Slezak, Ernestina Menasalvas Ruiz, Rafael Bello, Lin Shang
PublisherSpringer Nature
Pages35-46
Number of pages12
Volume8536 LNAI
ISBN (Print)9783319086439
DOIs
Publication statusPublished - 01 Jan 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8536 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Keywords

  • feature selection
  • fuzzy-rough sets
  • nearest neighbours

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