Optimization of the Regression Ensemble Size

Author:

Zelenkov Yuri

Abstract

Ensemble learning algorithms such as bagging often generate unnecessarily large models, which consume extra computational resources and may degrade the generalization ability. Pruning can potentially reduce ensemble size as well as improve performance; however, researchers have previously focused more on pruning classifiers rather than regressors. This is because, in general, ensemble pruning is based on two metrics: diversity and accuracy. Many diversity metrics are known for problems dealing with a finite set of classes defined by discrete labels. Therefore, most of the work on ensemble pruning is focused on such problems: classification, clustering, and feature selection. For the regression problem, it is much more difficult to introduce a diversity metric. In fact, the only such metric known to date is a correlation matrix based on regressor predictions. This study seeks to address this gap. First, we introduce the mathematical condition that allows checking whether the regression ensemble includes redundant estimators, i.e., estimators, whose removal improves the ensemble performance. Developing this approach, we propose a new ambiguity-based pruning (AP) algorithm that bases on error-ambiguity decomposition formulated for a regression problem. To check the quality of AP, we compare it with the two methods that directly minimize the error by sequentially including and excluding regressors, as well as with the state-of-art Ordered Aggregation algorithm. Experimental studies confirm that the proposed approach allows reducing the size of the regression ensemble with simultaneous improvement in its performance and surpasses all compared methods.

Publisher

SPIIRAS

Subject

Artificial Intelligence,Applied Mathematics,Computational Theory and Mathematics,Computational Mathematics,Computer Networks and Communications,Information Systems

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Managing Ambiguity in Regression Ensembles;2023 Ivannikov Ispras Open Conference (ISPRAS);2023-12-04

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