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Minimum class variance class-specific extreme learning machine for imbalanced classification

机译：用于实施分类的最小类别方案类别特定的极限学习机

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摘要

Imbalanced problems occur in real-world applications when the number of majority instances far exceeds the number of minority instances. Traditional extreme learning machine (ELM) classifier becomes biased towards the majority class due to imbalanced learning. To handle this inherent drawback, several modifications of ELM have been proposed such as weighted ELM (WELM), variances-constrained WELM (VW-ELM) to tackle the class imbalance problem effectively. One of our recent works class-specific ELM (CSELM) employs class-specific regularization and has been shown to outperform WELM for imbalanced learning. Motivated by CSELM, this work proposes a minimum class variance class-specific extreme learning machine (MCVCSELM), a variant of CSELM for tackling binary class imbalance problems more effectively. MCVCSELM uses the advantages of both the minimum class variance and the class-specific regularization. The proposed work also has lower computational complexity compared to WELM and VW-ELM. In class-specific cost regulation ELM (CCR-ELM), the calculation of the regularization parameters does not consider class distribution and class overlap. However, the performance of the CCR-ELM is comparable to ELM. MCVCSELM utilizes a class-specific regularization parameter whose value is decided by using the class proportion. The experimental results on 38 binary class datasets with different imbalanced ratios demonstrate that the proposed algorithm outperforms several state-of-the-art methods for imbalanced learning.
机译：当多数实例的数量远远超过少数群体实例的数量时，现实世界应用中发生了不平衡问题。由于学习不平衡，传统的极端学习机（ELM）分类器变为偏向于多数阶级。为了处理这种固有的缺点，已经提出了榆树的几种修改，例如加权ELM（WELM），差异 - 受限WELM（VW-ELM）有效地解决类别的不平衡问题。我们最近的作品类别的榆树（CSELM）采用了特定于类的正则化，并且已被证明以不平衡的学习来胜过WELM。由CSELM激励，这项工作提出了最小的类别方案类别特定的极限学习机（MCVCSELM），用于更有效地解决二元类不平衡问题的CSELM的变体。 MCVCSELM使用最小类方差和特定于类正则化的优点。与WELM和VW-ELM相比，拟议的工作也具有较低的计算复杂性。在特定于类别的成本调节ELM（CCR-ELM）中，正则化参数的计算不考虑类分布和类重叠。然而，CCR-ELM的性能与ELM相当。 McVcselm利用特定于类的正则化参数，其值通过使用类比例来决定。具有不同不平衡比例的38个二进制类数据集的实验结果表明，所提出的算法优于几种最先进的学习方法。

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