Extreme learning machine (ELM) is one of the most remarkable machine learning algorithm in consequence of superior properties particularly its speed. ELM algorithm tends to have some drawbacks like instability and poor generalization performance in the presence of perturbation and multicollinearity. This paper introduces a novel algorithm based on Liu regression estimator (L-ELM) to handle these drawbacks. Different selection approaches have been used to determine the appropriate Liu biasing parameter. The new algorithm is tested against the basic ELM, RR-ELM, AUR-ELM and OP-ELM on nine well-known benchmark data sets. Statistical significance tests have been carried out. Experimental results show that L-ELM for at least one Liu biasing parameter generally outperforms basic ELM, RR-ELM, AUR-ELM and OP-ELM in terms of stability and generalization performance with a little lost of speed. Conversely, the training time of L-ELM is generally much slower than RR-ELM, AUR-ELM and OP-ELM. Consequently, the proposed algorithm can be considered a powerful alternative to avoid the loss of performance in regression studies

中文翻译：

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基于刘氏回归的增强型极限学习机

极限学习机（ELM）由于其卓越的性能（尤其是其速度）而成为最杰出的机器学习算法之一。在存在扰动和多重共线性的情况下，ELM算法往往具有一些缺点，例如不稳定和泛化性能差。本文介绍了一种基于Liu回归估计器（L-ELM）的新颖算法来解决这些缺点。已经使用不同的选择方法来确定适当的Liu偏置参数。在9个著名的基准数据集上，针对基本ELM，RR-ELM，AUR-ELM和OP-ELM对新算法进行了测试。进行了统计显着性检验。实验结果表明，至少一个Liu偏置参数的L-ELM通常优于基本ELM，RR-ELM，在稳定性和泛化性能方面，AUR-ELM和OP-ELM几乎没有速度损失。相反，L-ELM的训练时间通常比RR-ELM，AUR-ELM和OP-ELM慢得多。因此，所提出的算法可以被认为是避免回归研究中性能损失的强大替代方法