Description and classification of granular time series

Rami Al-Hmouz*, Witold Pedrycz, Abdullah Balamash, Ali Morfeq

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

The study is concerned with a concept and a design of granular time series and granular classifiers. In contrast to the plethora of various models of time series, which are predominantly numeric, we propose to effectively exploit the idea of information granules in the description and classification of time series. The numeric (optimization-oriented) and interpretation abilities of granular time series and their classifiers are highlighted and quantified. A general topology of the granular classifier involving a formation of a granular feature space and the usage of the framework of relational structures (relational equations) in the realization of the classifiers is presented. A detailed design process is elaborated on along with a discussion of the pertinent optimization mechanisms. A series of experiments is covered leading to a quantitative assessment of the granular classifiers and their parametric analysis.

Original languageEnglish
Pages (from-to)1003-1017
Number of pages15
JournalSoft Computing
Volume19
Issue number4
DOIs
Publication statusPublished - Apr 2015
Externally publishedYes

Keywords

  • Classification rules
  • Fuzzy clustering
  • Fuzzy relational equation
  • Granular classifier
  • Human-centricity
  • Information granules
  • Interpretability
  • Time series

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology

Cite this