RRMSE Voting Regressor: A weighting function based improvement to ensemble regression

2022/07/11 by Shikun Chen, Chen, Shikun, Nguyen Manh Luc +1 · 1 citation
Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning and ELM #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2207.04837

openalex publication_date 2022/07/11 · openalex created_date 2023/02/12 · openalex updated_date 2026/07/28

Abstract

This paper describes the RRMSE (Relative Root Mean Square Error) based weights to weight the occurrences of predictive values before averaging for the ensemble voting regression. The core idea behind ensemble regression is to combine several base regression models in order to improve the prediction performance in learning problems with a numeric continuous target variable. The default weights setting for the ensemble voting regression is uniform weights, and without domain knowledge of learning task, assigning weights for predictions are impossible, which makes it very difficult to improve the predictions. This work attempts to improve the prediction of voting regression by implementing the RRMSE based weighting function. Experiments show that RRMSE voting regressor produces significantly better predictions than other state-of-the-art ensemble regression algorithms on six popular regression learning datasets.

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