Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Machine Learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Generating SAT local-search heuristics using a GP hyper-heuristic framework
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Classification Using Streaming Random Forests
IEEE Transactions on Knowledge and Data Engineering
| Hi-index | 0.00 |
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.