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Fuzzy twin support vector machine based on affinity and class probability for class imbalance learning

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Abstract

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 datapoints with higher AFs and CPs, while minimizing the influence of minority class samples with lower AFs and CPs. As a result, the decision boundary is skewed towards the MJ class. Five artificially imbalanced datasets and a few notable real-world datasets are used in numerical simulations.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

    Barenya Bikash Hazarika

  2. Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, India

    Deepak Gupta

  3. Department of Computer Science & Engineering, Indian Institute of Information Technology, Guwahati, Assam, India

    Parashjyoti Borah

Authors
  1. Barenya Bikash Hazarika
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  2. Deepak Gupta
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  3. Parashjyoti Borah
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Correspondence to Deepak Gupta.

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Hazarika, B.B., Gupta, D. & Borah, P. Fuzzy twin support vector machine based on affinity and class probability for class imbalance learning. Knowl Inf Syst 65, 5259–5288 (2023). https://doi.org/10.1007/s10115-023-01904-8

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