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

TL;DR

This paper introduces an evolving non-linear stacking ensemble method utilizing evolutionary algorithms to predict Go player attributes like strength and style from game data, enhancing prediction accuracy.

Contribution

It proposes a novel combination of evolutionary algorithms with non-linear stacking ensembles for predicting player attributes in Go, which is a new approach in this domain.

Findings

Effective prediction of Go player attributes achieved

Diverse ensemble formation improves accuracy

Method demonstrates efficiency in attribute prediction

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.