Dataset condensation using OWA fuzzy-rough set-based nearest neighbor classifier

Mehran Amiri*, Richard Jensen, Mahdi Eftekhari, Neil MacParthaláin

*Corresponding author for this work

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

5 Citations (SciVal)

Abstract

The application of fuzzy-rough sets for the task of feature selection and rule induction has been the topic of much interest recently. However, applications for data instance or object selection have attracted much less attention. In this paper a novel approach for dataset condensation based on ordered weighted aggregation (OWA) fuzzy-rough sets is proposed in the context of the KNN classifier. Initially, a rank is assigned to each data instance of the dataset, based upon a novel measure inspired by fuzzy-rough sets. High quality data instances which possess higher ranks are retained based on another new metric whilst others can then be removed. An additional novel innovation is the elimination of any subjective user-specified threshold in order to determine which particular data instances are candidates for removal. This is in keeping with the rough set ideology of data-driven approaches. A series of non-parametric statistical tests demonstrate that the technique is very effective and can produce useful condensations of the data.

Original languageEnglish
Title of host publication2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
PublisherIEEE Press
Pages1934-1941
Number of pages8
ISBN (Electronic)978-1-5090-0626-7
DOIs
Publication statusPublished - 2016
EventFuzzy Systems - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016
Conference number: 25

Publication series

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

Conference

ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2016
Country/TerritoryCanada
CityVancouver
Period24 Jul 201629 Jul 2016

Keywords

  • Dataset condensation
  • OWA Fuzzy-rough sets
  • Fuzzy-rough prototype selection
  • Fuzzy-rough nearest neighbor
  • PROTOTYPE SELECTION
  • ALGORITHMS
  • EVOLUTIONARY

Fingerprint

Dive into the research topics of 'Dataset condensation using OWA fuzzy-rough set-based nearest neighbor classifier'. Together they form a unique fingerprint.

Cite this