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

Roshani Choudhary,Sanyam Shukla

doi:10.3390/sym14020379

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.

Highlights

The performance of a classification problem is affected by various data complexity measures such as class imbalance, class overlapping, length of the decision boundary, small disjuncts of classes, etc
The proposed Reduced-Kernelized Weighted Extreme Learning Machine (RKWELM)-UFS is compared with three sets of algorithms used to handle class imbalance learning
The second set of approaches consists of the single classifiers such as KELM [46], WKELM [18], CCR-KELM [19], and Weighted Kernelized Synthetic Minority Oversampling Technique (WKSMOTE) [23] which are used to handle class-imbalanced problems

Summary

Introduction

The performance of a classification problem is affected by various data complexity measures such as class imbalance, class overlapping, length of the decision boundary, small disjuncts of classes, etc. Most of the real-world problems are class imbalanced. Examples of such problems are cancer detection [1,2], fault detection [3], intrusion detection system [4], software test optimization [5], speech quality assessment [6], pressure prediction [7], etc. In a problem when the number of samples in one class outnumbers the numbers of samples in some other class, it is considered as a class imbalanced/asymmetric problem. The class with a greater number of instances is the majority class and the class with fewer instances is the minority class. In real-world problems, usually, the minority class instances have more importance than the majority class

Methods

Results

Conclusion