Affinity based fuzzy kernel ridge regression classifier for binary class imbalance learning. (January 2023)

• Record Type: Journal Article
• Authors: Hazarika, Barenya Bikash; Gupta, Deepak
• Is Part Of: Engineering applications of artificial intelligence. Volume 117:Part A(2023)
• Publication Date: 2023-01
• Subjects: Kernel ridge regression -- Affinity -- Classification -- Support vector data description; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105544
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The class imbalance learning (CIL) problem indicates when one class have very low proportions of samples (minority class) compared to the other class (majority class). Even though kernel ridge regression (KRR) shows high generalization ability at a considerably quicker learning speed than conventional machine learning algorithms, it fails to achieve an excellent result for CIL problems. To address this inherent limitation of KRR, a novel affinity-based fuzzy KRR (AFKRR) is proposed for dealing with the binary CIL problem. In AFKRR, an affinity-based fuzzy membership value is linked to each training sample. The affinity of the majority class datapoint is measured using the support vector data description (SVDD) trained by the majority class datapoints. The classification ability of the proposed AFKRR is calculated using the area under the receiver optimal characteristics curve (AUC), F-measure and Geometric mean. AFKRR's performance is compared with the support vector machine (SVM), affinity and class probability-based fuzzy SVM (ACFSVM), entropy-based fuzzy SVM (EFSVM), improved density weighted least squares SVM (IDLSSVM-CIL), KRR and intuitionistic fuzzy KRR (IFKRR) models on a few real-world as well as a few artificial imbalanced datasets. Experimental outcomes reveal the usability and efficacy of the proposed AFKRR model.