In machine learning, a problem is imbalanced when the class distributions are highly skewed. Imbalanced classification problems occur usually in many application domains and pose a hindrance to the conventional learning algorithms. Several approaches have been proposed to handle the imbalanced learning. For example, Weighted kernel-based SMOTE (WKSMOTE) and SMOTE based class-specific extreme learning machine (SMOTE-CSELM) are recently proposed algorithms that use the minority oversampling to handle imbalanced learning. It has been illustrated in Raghuwanshi and Shukla (Knowl-Based Syst 187(104):814, 2020) that our recently proposed classifier, SMOTE-CSELM outperforms the other state of art classifiers for class imbalance learning. One drawback of SMOTE-CSELM is the performance fluctuation due to the random initialization of weights between the input and the hidden layer. To handle this problem, this work proposes SMOTE based class-specific kernelized extreme learning machine (SMOTE-CSKELM), which uses the Gaussian kernel function to map the input data to the feature space. The proposed work has the advantage of both the minority oversampling and the class-specific regularization coefficients. SMOTE-CSKELM with the Gaussian kernel function also handles the non-optimal hidden node problem associated with the sigmoid node based variants of ELM. To increase the significance of the specific region corresponding to the minority class in the decision boundary, the synthetic minority oversampling technique (SMOTE) is applied to generate synthetic instances for the minority class to balance the training dataset. The proposed work has comparable training time in contrast with the kernelized weighted extreme learning machine (KWELM) for imbalanced learning. The proposed method is determined by employing benchmark real-world imbalanced datasets. The extensive experimental results report that the proposed method outperforms compared to the other state-of-the-art methods for imbalanced learning.

在机器学习中，当班级分布高度偏斜时，问题是不平衡的。分类失衡问题通常发生在许多应用领域，并给传统的学习算法带来了障碍。已经提出了几种方法来处理不平衡学习。例如，最近提出了基于加权内核的SMOTE（WKSMOTE）和基于SMOTE的类特定的极端学习机（SMOTE-CSELM），这些算法使用少数过采样来处理不平衡学习。在Raghuwanshi和Shukla（基于Knowl的Syst 187（104）：814，2020）中已经说明，我们最近提出的分类器SMOTE-CSELM在分类失衡学习方面优于其他分类器。SMOTE-CSELM的一个缺点是由于输入和隐藏层之间权重的随机初始化而导致的性能波动。为了解决这个问题，这项工作提出了一种基于SMOTE的基于类的特定内核极限学习机（SMOTE-CSKELM），它使用高斯核函数将输入数据映射到特征空间。拟议的工作具有少数过采样和特定于类别的正则化系数的优点。具有高斯核函数的SMOTE-CSKELM还可以处理与基于Sigmoid节点的ELM变体相关的非最优隐藏节点问题。为了增加决策边界中与少数类相对应的特定区域的重要性，综合少数群体过采样技术（SMOTE）用于为少数群体类别生成综合实例，以平衡训练数据集。与用于不平衡学习的核加权加权极限学习机（KWELM）相比，拟议的工作具有可比的培训时间。通过采用基准现实世界中的不平衡数据集来确定所提出的方法。大量的实验结果表明，与其他最先进的不平衡学习方法相比，该方法的性能优于其他方法。