用于空气质量预测的集合在线序列极限学习机

Ye Liu, Weipeng Cao, Yiwen Liu, Dachuan Li, Qiang Wang
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引用次数: 1

摘要

在线顺序极限学习机(OS-ELM)是一种有效的在线学习算法,已被大量研究证实。然而,我们发现OS-ELM的一些参数是随机分配的,并且在随后的学习过程中保持不变,这导致模型在实践中的性能有很大的不稳定性。为了解决这一问题,我们提出了一种新的集成OS-ELM算法(EOS-ELM-R)来解决空气质量预测问题。EOS-ELM-R使用多个分布函数初始化基本OS-ELM模型的随机参数,其最终输出是这些基本模型预测的平均值。在两个现实世界空气质量预测问题上的大量实验结果表明,EOS-ELM-R是有效的,并且比同类算法具有更好的泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ensemble Online Sequential Extreme Learning Machine for Air Quality Prediction
Online Sequential Extreme Learning Machine (OS-ELM) has been confirmed by numerous studies to be an effective algorithm for online learning scenarios. However, we found that some parameters of OS-ELM are randomly assigned and remain unchanged in the subsequent learning process, which leads to great instability in the model performance in practice. To alleviate this problem, we propose a novel ensemble OS-ELM algorithm (EOS-ELM-R) for solving air quality prediction problems. EOS-ELM-R uses multiple distribution functions to initialize the random parameters of the base OS-ELM models and its final output is the average of the predictions of these base models. Extensive experimental results on two real-world air quality prediction problems show that EOS-ELM-R is effective, and it can achieve better generalization capabilities than similar algorithms.
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