Evolving Non-linear Stacking Ensembles for Prediction of Go Player
  Attributes
  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.
