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Fuzzy Twin Support Vector Machine Based on Affinity and Class Probability for Class Imbalance Learning by Barenya Bikash Hazarika; Deepak Gupta; Parashjyoti Borah is a Computer Science article available to read on EtoBox.

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Recently a robust and efficient classifier termed affinity and class probability-based fuzzy support vector machine (ACFSVM) was proposed to address the binary class imbalance and noisy data classification problems. Despite the excellent generalization ability of ACFSVM, there is a scope to improve its classification ability. To enhance the classification performance of ACFSVM, this work suggests a novel fuzzy twin support vector machine based on affinity and class probability (ACFTSVM). In ACFTSVM, regularization terms are added to the primal problems which diminish the negative influence of noise. The affinity (AF) of the majority (MJ) class datapoints is measured using the support vector data description model trained in kernel space using only the MJ class training samples. The k-nearest neighbour method is used to estimate the class probability (CP) of the MJ class datapoints in the same kernel space as before to decrease the potential of noises. Lower CP samples are prone to noise, and their contribution to learning appears to be harmed by their low memberships, which are calculated by adding the AFs and the CPs. ACFTSVM, like ACFSVM, will give preference to MJ class datapoin

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Author

Barenya Bikash Hazarika; Deepak Gupta; Parashjyoti Borah

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)