Abstract
It has been demonstrated how random subspaces can be used to create a Diversified Random Forest, which in turn can lead to better performance in terms of predictive accuracy. Motivated by the fact that each subsforest is built using a set of features that can overlap with those sets of features in other subforests, we hypothesise that using Replicator Dynamics can perform a collective feature engineering, by allowing subforests with better performance to grow and those with lower performance to shrink. In this paper, we propose a new method to further improve the performance of Diversified Random Forest using Replicator Dynamics which has been used extensively in evolutionary game dynamics. A thorough experimental study on 15 real datasets showed favourable results, demonstrating the potential of the proposed method. Some experiments reported a boost in predictive accuracy of over 10 % consistently, evidencing the effectiveness of the iterative feature engineering achieved through the Replicator Dynamics procedure.
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Fawgreh, K., Gaber, M.M., Elyan, E. (2015). A Replicator Dynamics Approach to Collective Feature Engineering in Random Forests. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXII. SGAI 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-25032-8_2
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DOI: https://doi.org/10.1007/978-3-319-25032-8_2
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