An evolutionary cut points search for graph clustering-based discretization

Kittakorn Sriwanna, Tossapon Boongoen, Natthakan Iam-On

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

3 Dyfyniadau(SciVal)

Crynodeb

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.

Iaith wreiddiolSaesneg
Teitl2016 13th International Joint Conference on Computer Science and Software Engineering, JCSSE 2016
CyhoeddwrIEEE Press
ISBN (Electronig)9781509020331
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 18 Tach 2016
Digwyddiad13th International Joint Conference on Computer Science and Software Engineering, JCSSE 2016 - Khon Kaen, Gwlad Thai
Hyd: 13 Gorff 201615 Gorff 2016

Cyfres gyhoeddiadau

Enw2016 13th International Joint Conference on Computer Science and Software Engineering, JCSSE 2016

Cynhadledd

Cynhadledd13th International Joint Conference on Computer Science and Software Engineering, JCSSE 2016
Gwlad/TiriogaethGwlad Thai
DinasKhon Kaen
Cyfnod13 Gorff 201615 Gorff 2016

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