Evolving Non-linear Stacking Ensembles for Prediction of Go Player Attributes
This work addresses the specific problem of attribute prediction for Go players, but it appears incremental as it applies existing ensemble methods to a new domain without claiming broad advancements.
The paper tackled the problem of predicting Go player attributes like strength and style from game data by using an evolutionary algorithm to create a diverse ensemble of base learners, aggregated through non-linear stacking, achieving efficient prediction.
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.