An evolutionary cut points search for graph clustering-based discretization

Kittakorn Sriwanna, Tossapon Boongoen, Natthakan Iam-On

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

3 Citations (SciVal)

Abstract

Most discretization algorithms focus on the univariate case, which proceeds without considering interactions among attributes. Furthermore, they find the value of discretization criterion based on a greedy method that usually leads to a sub-optimal set of cut-points. In response, this paper introduces an evolutionary cut points search for graph clustering-based discretization (GraphE). The resulting method exhibits both multivariate and searching properties. The proposed evolutionary model is based on Genetic algorithm with the specifically designed fitness function. It simultaneously considers the data similarity via the notion of normalized association and the number of intervals. The proposed method is compared with 7 state-of-the-art discretization algorithms, conducted on 15 datasets and 3 classifier models. The results suggest that the new technique usually achieves higher classification accuracy than the comparative methods, while requiring less computational time than the existing optimization-based model.

Original languageEnglish
Title of host publication2016 13th International Joint Conference on Computer Science and Software Engineering, JCSSE 2016
PublisherIEEE Press
ISBN (Electronic)9781509020331
DOIs
Publication statusPublished - 18 Nov 2016
Event13th International Joint Conference on Computer Science and Software Engineering, JCSSE 2016 - Khon Kaen, Thailand
Duration: 13 Jul 201615 Jul 2016

Publication series

Name2016 13th International Joint Conference on Computer Science and Software Engineering, JCSSE 2016

Conference

Conference13th International Joint Conference on Computer Science and Software Engineering, JCSSE 2016
Country/TerritoryThailand
CityKhon Kaen
Period13 Jul 201615 Jul 2016

Keywords

  • genetic algorithms
  • biological cells
  • clustering algorithms
  • encoding
  • sociology
  • statistics
  • computer science

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