Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS) to Handle Binary Class Imbalanced Dataset Classification

Abstract

Class imbalance is a phenomenon of asymmetry that degrades the performance of traditional classification algorithms such as the Support Vector Machine (SVM) and Extreme Learning Machine (ELM). Various modifications of SVM and ELM have been proposed to handle the class imbalance problem, which focus on different aspects to resolve the class imbalance. The Universum Support Vector Machine (USVM) incorporates the prior information in the classification model by adding Universum data to the training data to handle the class imbalance problem. Various other modifications of SVM have been proposed which use Universum data in the classification model generation. Moreover, the existing ELM-based classification models intended to handle class imbalance do not consider the prior information about the data distribution for training. An ELM-based classification model creates two symmetry planes, one for each class. The Universum-based ELM classification model tries to create a third plane between the two symmetric planes using Universum data. This paper proposes a novel hybrid framework called Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS) to handle the classification of binary class-imbalanced problems. The proposed RKWELM-UFS combines the Universum learning method with a Reduced-Kernelized Weighted Extreme Learning Machine (RKWELM) for the first time to inherit the advantages of both techniques. To generate efficient Universum samples in the feature space, this work uses the kernel trick. The performance of the proposed method is evaluated using 44 benchmark binary class-imbalanced datasets. The proposed method is compared with 10 state-of-the-art classifiers using AUC and G-mean. The statistical t-test and Wilcoxon signed-rank test are used to quantify the performance enhancement of the proposed RKWELM-UFS compared to other evaluated classifiers.