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This paper aims to evaluate the impact of the various optimization algorithms used in methods based on logistic regression fitting in positive and unlabeled classification under the SCAR assumption. In our work, we consider the joint algorithm based on logistic regression. The impact of non-linear optimization algorithms and simulated annealing technique is examined on eleven real data sets from machine learning repositories and one synthetic set. To assess this impact, we evaluate the AUC, accuracy, recall, precision and F1 score of the classification for the naive and the joint method.

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Positive Unlabeled Classification Methods with Logistic Regression Revisited: An Evaluation of Optimization Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Kacper Paczutkowski,
  • Konrad Furmańczyk

摘要

This paper aims to evaluate the impact of the various optimization algorithms used in methods based on logistic regression fitting in positive and unlabeled classification under the SCAR assumption. In our work, we consider the joint algorithm based on logistic regression. The impact of non-linear optimization algorithms and simulated annealing technique is examined on eleven real data sets from machine learning repositories and one synthetic set. To assess this impact, we evaluate the AUC, accuracy, recall, precision and F1 score of the classification for the naive and the joint method.