Fuzzy least squares projection twin support vector machines for class imbalance learning

• Published in: Applied soft computing Vol. 113; p. 107933
• Main Authors: Ganaie, M.A., Tanveer, M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2021
• Subjects: Alzheimer’s disease (AD); Breast Cancer; Class imbalance; Fuzzy membership; Imbalance ratio; Least squares twin support vector machine; Magnetic resonance imaging (MRI); Outliers; Projections; Support vector machines; Twin support vector machines; Support vector machines; Outliers; Least squares twin support vector machine; Alzheimer’s disease (AD); Fuzzy membership; Class imbalance; Twin support vector machines; Magnetic resonance imaging (MRI); Projections; Imbalance ratio; Breast Cancer
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2021.107933

In this paper, we propose a novel fuzzy least squares projection twin support vector machines for class imbalance learning (FLSPTSVM-CIL). Unlike twin support vector machine (TSVM) which solves two dual problems, we solve two modified primal formulations by solving two systems of linear equations. The proposed FLSPTSVM-CIL model seeks two projection directions such that the samples of two classes are well separated in the projected space. To avoid the singularity issues, we incorporate an extra regularization term to make the optimization problem positive definite. As the real world data may be imbalanced, we assign appropriate fuzzy weights to the samples such that the classifier is not biased towards the samples of the majority class. The statistical analysis and experimental results on the publicly available UCI benchmark datasets show that the proposed FLSPTSVM-CIL performs better as compared to the baseline models. To show the applications of the proposed FLSPTSVM-CIL model on real world datasets, we performed classification of Alzheimer’s disease and breast cancer patients. Experimental results show that the generalization performance of the proposed FLSPTSVM-CIL model for the classification of the breast cancer patients and the mild cognitive impairment versus Alzheimer’s disease subjects is better as compared to the baseline models. •A novel fuzzy least squares projection twin SVM is proposed for class imbalance learning.•The proposed algorithm implements the structural risk minimization principle and the matrices appear in the proposed formulation are positive definite.•The proposed model seeks projections such that the samples of each class are clustered around its corresponding mean and the samples of different classes are as far as possible.•Fuzzy weights are assigned in the proposed model to handle the class imbalance problems.•Applications of proposed models are shown on the classification of breast cancer and Alzheimer’s disease subjects.