GARF: towards self-optimised random forests

Mohamed Bader, M. Gaber

Research output: Contribution to conferencePaperpeer-review

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Abstract

Ensemble learning is a machine learning approach that utilises a number of classifiers to contribute via voting to identifying the class label for any unlabelled instances. Random Forests RF is an ensemble classification approach that has proved its high accuracy and superiority. However, most of the commonly used selection methods are static. Motivated by the idea of having self-optimised RF capable of dynamical changing the trees in the forest. This study uses a genetic algorithm GA approach to further enhance the accuracy of RF. The approach is termed as Genetic Algorithm based RF (GARF). Our extensive experimental study has proved that RF performance is be boosted using the GA approach.
Original languageEnglish
Publication statusPublished - Nov 2012
EventThe 19th International Conference on Neural Information Processing - Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Conference

ConferenceThe 19th International Conference on Neural Information Processing
Abbreviated titleICONIP2012
Country/TerritoryQatar
CityDoha
Period12/11/1215/11/12

Keywords

  • Random Forest
  • Genetic Algorithms
  • Ensemble Classification

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