Evolving Non-linear Stacking Ensembles for Prediction of Go Player Attributes
arXiv:1512.09254 · doi:10.1109/SSCI.2015.235
Abstract
The paper presents an application of non-linear stacking ensembles for prediction of Go player attributes. An evolutionary algorithm is used to form a diverse ensemble of base learners, which are then aggregated by a stacking ensemble. This methodology allows for an efficient prediction of different attributes of Go players from sets of their games. These attributes can be fairly general, in this work, we used the strength and style of the players.
Published in 2015 IEEE Symposium Series on Computational Intelligence