Flexible multi-class cost-sensitive thresholding

Abstract 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.