Imbalanced learning is one of the substantial challenging problems in the field of data mining. The datasets that have skewed class distribution pose hindrance to conventional learning methods. Conventional learning methods give the same importance to all the examples. This leads to the prediction inclined in favor of the majority classes. To solve this intrinsic deficiency, numerous strategies have been proposed such as weighted extreme learning machine (WELM) and boosting WELM (BWELM). This work designs a novel BalanceCascade-based kernelized extreme learning machine (BCKELM) to tackle the class imbalance problem more effectively. BalanceCascade includes the merits of random undersampling and the ensemble methods. The proposed method utilizes random undersampling to design balanced training subsets. The proposed ensemble generates the base learner in a sequential manner. In each iteration, the correctly classified examples belonging to the majority class are replaced by the other majority class examples to create a new balanced training subset, i.e., the base learners differ in the choice of the balanced training subset. The cardinality of the balanced training subsets depends on the imbalance ratio. This work utilizes a kernelized extreme learning machine (KELM) as the base learner to build the ensemble as it is stable and has good generalization performance. The time complexity of BCKELM is considerably lower in contrast to BWELM, BalanceCascade, EasyEnsemble and hybrid artificial bee colony WELM. The exhaustive experimental evaluation on real-world benchmark datasets demonstrates the efficacy of the proposed method.

中文翻译：

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使用基于BalanceCascade的内核化极限学习机对不平衡数据进行分类

学习不平衡是数据挖掘领域中具有挑战性的重大问题之一。班级分布偏斜的数据集阻碍了传统的学习方法。常规学习方法对所有示例都具有相同的重要性。这导致倾向于多数阶级的预测。为了解决这种内在缺陷，已经提出了许多策略，例如加权极限学习机（WELM）和增强WELM（BWELM）。这项工作设计了一种新颖的基于BalanceCascade的内核化极限学习机（BCKELM），以更有效地解决班级失衡问题。BalanceCascade包括随机欠采样和集成方法的优点。所提出的方法利用随机欠采样来设计平衡训练子集。所提出的整体以顺序的方式生成基础学习者。在每次迭代中，将属于多数类别的正确分类的示例替换为其他多数类别示例，以创建新的平衡训练子集，即基础学习者在平衡训练子集的选择上有所不同。平衡训练子集的基数取决于不平衡率。这项工作利用一个内核化的极限学习机（KELM）作为基础学习器来构建集成体，因为它稳定并且具有良好的泛化性能。与BWELM，BalanceCascade，EasyEnsemble和混合人工蜂群WELM相比，BCKELM的时间复杂度要低得多。对真实基准数据集的详尽实验评估证明了该方法的有效性。