Evolutionary bagging for ensemble learning
This work addresses the need for more diverse and effective ensemble learning methods in machine learning, though it appears incremental as it builds on existing bagging and evolutionary algorithm techniques.
The paper tackles the problem of fixed bag content in conventional bagging by proposing evolutionary bagging, which uses evolutionary algorithms to evolve bag content iteratively for enhanced diversity. The results show that this method outperforms conventional bagging and random forests on several benchmark datasets under certain constraints, sustaining diversity without reducing accuracy.
Ensemble learning has gained success in machine learning with major advantages over other learning methods. Bagging is a prominent ensemble learning method that creates subgroups of data, known as bags, that are trained by individual machine learning methods such as decision trees. Random forest is a prominent example of bagging with additional features in the learning process. Evolutionary algorithms have been prominent for optimisation problems and also been used for machine learning. Evolutionary algorithms are gradient-free methods that work with a population of candidate solutions that maintain diversity for creating new solutions. In conventional bagged ensemble learning, the bags are created once and the content, in terms of the training examples, are fixed over the learning process. In our paper, we propose evolutionary bagged ensemble learning, where we utilise evolutionary algorithms to evolve the content of the bags in order to iteratively enhance the ensemble by providing diversity in the bags. The results show that our evolutionary ensemble bagging method outperforms conventional ensemble methods (bagging and random forests) for several benchmark datasets under certain constraints. We find that evolutionary bagging can inherently sustain a diverse set of bags without reduction in performance accuracy.