Attribute Reduction Based on MapReduce Model and Discernibility Measure - Computer Information Systems and Industrial Management (CISIM 2016)
Conference Papers Year : 2016

Attribute Reduction Based on MapReduce Model and Discernibility Measure

Michal Czolombitko
  • Function : Author
  • PersonId : 1023064
Jaroslaw Stepaniuk
  • Function : Author
  • PersonId : 1023034

Abstract

This paper discusses two important problems of data reduction. The problems are related to computing reducts and core in rough sets. The authors use the fact that the necessary information about discernibility matrices can be computed directly from data tables, in the case of this paper so called counting tables are used. The discussed problems are of high computational complexity. Hence the authors propose to use the relevant heuristics, MRCR (MapReduce Core and Reduct Generation) implemented using the MapReduce model.
Fichier principal
Vignette du fichier
419526_1_En_6_Chapter.pdf (255.08 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01637503 , version 1 (17-11-2017)

Licence

Identifiers

Cite

Michal Czolombitko, Jaroslaw Stepaniuk. Attribute Reduction Based on MapReduce Model and Discernibility Measure. 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Sep 2016, Vilnius, Lithuania. pp.55-66, ⟨10.1007/978-3-319-45378-1_6⟩. ⟨hal-01637503⟩
52 View
106 Download

Altmetric

Share

More