Evolutionarily optimized rough sets partitions

Bodie Crossingham, Tshilidzi Marwala, Monica Lagazio

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


In this paper, an approach to optimally granulize rough set partition sizesusing evolutionary techniques is proposed. The evolutionary optimizationtechniques used are genetic algorithm particle swarm optimization and simulatedannealing. These evolutionary methods are compared to the hill climbingtechnique on an interstate conflict dataset. The results obtained from thisgranulization process are compared to the equal-width-bin andequal-frequency-bin partitioning techniques. The results obtained demonstratethat all of the proposed optimized methods produce higher forecasting accuraciesthan that of the two static methods and that genetic algorithm approach producethe highest accuracy. It is observed that the rules generated from the roughsets are linguistic and easy-to-interpret, but this does come at the expense ofloss of accuracy in the discretization process where the granularity of thevariables is decreased. ICIC International

Original languageEnglish
Pages (from-to)241-246
Number of pages6
JournalICIC Express Letters
Issue number3
Publication statusPublished - Sept 2009


  • Evolutionary optimization
  • Granulization
  • Interstate conflict
  • Rough sets

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science


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