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

arXiv: https://arxiv.org/abs/1512.09254 · HTML: https://mlsanity.com/paper/1512.09254/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2015.

## Built on by (most cited first, 2 of 2)

- [The ProfessionAl Go annotation datasEt (PAGE)](https://mlsanity.com/paper/2211.01559/) (2022)
- [Policies of Multiple Skill Levels for Better Strength Estimation in Games](https://mlsanity.com/paper/2505.00279/) (2025)

## Abstract (arXiv)

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.