Ordering of functions according to multiple fuzzy criteria: application to denoising electroencephalography

Andrea Burgos-Madrigal, Felipe Orihuela-Espina, Carlos Alberto Reyes-García

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce a new relation of order over functions according to multiple fuzzy criteria. Proof of the complied properties for relations of partial orders is given. Convergent and divergent validity of the new membership functions is established. Tolerance to noise of the relation of order is evaluated by corrupting synthetic prototypes and observing changes in the retrieved ordering. The effect of weighting strategies is evaluated in terms of Jaccard and XOR indices. The performance of the ordering algorithm is quantified in terms of richness of the resulting Hasse diagram. Applicability is demonstrated in the context of de-noising electroencephalographic (EEG) signals exemplified over two datasets and evaluated by classification wrapping.
Original languageEnglish
Pages (from-to)8573-8593
Number of pages21
JournalSoft Computing
Volume25
Issue number13
Early online date18 Mar 2021
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Electroencephalography
  • Fuzzy decision making
  • Fuzzy order relations
  • Membership functions
  • Multiple criteria evaluation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology

Fingerprint

Dive into the research topics of 'Ordering of functions according to multiple fuzzy criteria: application to denoising electroencephalography'. Together they form a unique fingerprint.

Cite this