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.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionSimilar Papers 제목 키워드 기반
Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators
The uncertainties in future Bitcoin price make it difficult to accurately predict the price of Bitcoin. Accurately predicting the price for Bitcoin is therefore important for decision-making process of investors and mark…
BIG-bench Machine LearningDecision MakingregressionA Human-Centered Approach for Improving Supervised Learning
Supervised Learning is a way of developing Artificial Intelligence systems in which a computer algorithm is trained on labeled data inputs. Effectiveness of a Supervised Learning algorithm is determined by its performanc…
Ensemble LearningSystematic Ensemble Learning for Regression
The motivation of this work is to improve the performance of standard stacking approaches or ensembles, which are composed of simple, heterogeneous base models, through the integration of the generation and selection sta…
Ensemble LearningregressionA Generalized Stacking for Implementing Ensembles of Gradient Boosting Machines
The gradient boosting machine is one of the powerful tools for solving regression problems. In order to cope with its shortcomings, an approach for constructing ensembles of gradient boosting models is proposed. The main…
regressionOptimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems
Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution of the data more accurately. Different ensembling methods, such as bagging, boosting, an…
BIG-bench Machine Learningregression