Parallel Algorithm for Optimal Threshold Labeling of Ordinal Regression Methods
<|reference_start|>Parallel Algorithm for Optimal Threshold Labeling of Ordinal Regression Methods: Ordinal regression (OR) is classification of ordinal data in which the underlying categorical target variable has a natural ordinal relation for the underlying explanatory variable. For $K$-class OR tasks, threshold methods learn a one-dimensional transformation (1DT) of the explanatory variable so that 1DT values for observations of the explanatory variable preserve the order of label values $1,\ldots,K$ for corresponding observations of the target variable well, and then assign a label prediction to the learned 1DT through threshold labeling, namely, according to the rank of an interval to which the 1DT belongs among intervals on the real line separated by $(K-1)$ threshold parameters. In this study, we propose a parallelizable algorithm to find the optimal threshold labeling, which was developed in previous research, and derive sufficient conditions for that algorithm to successfully output the optimal threshold labeling. In a numerical experiment we performed, the computation time taken for the whole learning process of a threshold method with the optimal threshold labeling could be reduced to approximately 60\,\% by using the proposed algorithm with parallel processing compared to using an existing algorithm based on dynamic programming.<|reference_end|>
@article{yamasaki2024parallel,
    title={Parallel Algorithm for Optimal Threshold Labeling of Ordinal Regression
  Methods},
    author={Ryoya Yamasaki and Toshiyuki Tanaka},
    journal={arXiv preprint arXiv:2405.12756},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12756},
    primaryClass={cs.LG}
}