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.
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Bader-El-Den, M., Gaber, M. (2012). GARF: Towards Self-optimised Random Forests. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_62
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DOI: https://doi.org/10.1007/978-3-642-34481-7_62
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