SMOTE based class-specific extreme learning machine for imbalanced learning

摘要

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 samples. This leads to biased accuracy, which favors the majority classes. Several classifiers have been designed to tackle the class imbalance problems. Weighted kernel-based SMOTE (WKSMOTE) is a recently proposed method, which employs the minority oversampling in kernel space to tackle the class imbalance problem. Motivated by WKSMOTE, this work proposes a novel SMOTE based class-specific extreme learning machine (SMOTE-CSELM), a variant of class-specific extreme learning machine (CS-ELM), which exploits the benefit of both the minority oversampling and the class-specific regularization. For minority oversampling, this work uses synthetic minority oversampling technique (SMOTE). It increases the significance of the minority class samples for determining the decision region of the classifiers. The proposed method has comparable computational complexity than the weighted extreme learning machine (WELM) for imbalanced learning. The extensive experimental results evaluated on the real-world benchmark datasets demonstrate the efficacy of our proposed method.