Evolving Non-Linear Stacking Ensembles for Prediction of Go Player Attributes

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.

Keywords

• learning (artificial intelligence)
• Go player attributes prediction
• computer Go
• evolutionary algorithm
• machine-learning methodology
• nonlinear stacking ensembles
• Bagging
• Biological neural networks
• Feature extraction
• Games
• Genetic algorithms
• Stacking