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<p>In machine learning, models are learned from training data with labels denoting the assigned names or classes and features representing characteristics of samples. However, data collection may introduce noise which can significantly impact the performance of these algorithms. In this paper, to take benefits of the computational efficiency of dual support vector machine (SVM) model, first we implement relabeling on the dual SVM problem resulting in a mixed-integer program (MIP) with less variables and constraints compared to the primal relabeled SVM (RESVM) model in the literature. Then we propose another dual SVM-based MIP model called cluster dual RESVM (CDRESVM), wherein instances are relabeled based on their proximity to the nearest class by using binary variables and new set of constraints. Experiments on 10 UCI and 2 Microarray datasets show that the proposed dual SVM-based models exhibit higher accuracy and better learning time compared to the primal SVM models, on the majority of datasets.</p>

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A Dual SVM Approach to Noisy Labels Relabeling

  • 期刊论文

  • 发表日期：

    2025年1月27日

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  • Ali Sahleh,
  • Maziar Salahi,
  • Sadegh Eskandari

摘要

In machine learning, models are learned from training data with labels denoting the assigned names or classes and features representing characteristics of samples. However, data collection may introduce noise which can significantly impact the performance of these algorithms. In this paper, to take benefits of the computational efficiency of dual support vector machine (SVM) model, first we implement relabeling on the dual SVM problem resulting in a mixed-integer program (MIP) with less variables and constraints compared to the primal relabeled SVM (RESVM) model in the literature. Then we propose another dual SVM-based MIP model called cluster dual RESVM (CDRESVM), wherein instances are relabeled based on their proximity to the nearest class by using binary variables and new set of constraints. Experiments on 10 UCI and 2 Microarray datasets show that the proposed dual SVM-based models exhibit higher accuracy and better learning time compared to the primal SVM models, on the majority of datasets.