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<p>Classification involves categorizing input data into predefined classes based on their characteristics. Thresholding methods predict the optimal class for an observation given a score and a missclassification error cost specification. In multi-class classification, existing algorithms assume that a score is available for each possible response. However, there are scenarios where more classes can be predicted than the underlying response variable has. This paper extends the flexibility of the 2-DDR algorithm introduced by C-Rella et al. (Inf Sci 657:119956;2024) to the multi-class classification problem. The proposed method predicts the optimal classification in cost-sensitive multi-class problems considering a single score fitted over a binary variable, a problem not previously studied. Furthermore, a more efficient version of the algorithm is proposed. The good performance of the proposed multi-class method is demonstrated through extensive simulations and the analysis of four real data sets.</p>

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Flexible multi-class cost-sensitive thresholding

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月22日

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  • Jorge C-Rella,
  • Juan M. Vilar

摘要

Classification involves categorizing input data into predefined classes based on their characteristics. Thresholding methods predict the optimal class for an observation given a score and a missclassification error cost specification. In multi-class classification, existing algorithms assume that a score is available for each possible response. However, there are scenarios where more classes can be predicted than the underlying response variable has. This paper extends the flexibility of the 2-DDR algorithm introduced by C-Rella et al. (Inf Sci 657:119956;2024) to the multi-class classification problem. The proposed method predicts the optimal classification in cost-sensitive multi-class problems considering a single score fitted over a binary variable, a problem not previously studied. Furthermore, a more efficient version of the algorithm is proposed. The good performance of the proposed multi-class method is demonstrated through extensive simulations and the analysis of four real data sets.