Parallel Algorithm for Optimal Threshold Labeling of Ordinal Regression Methods

TL;DR

This paper introduces a parallel algorithm for optimal threshold labeling in ordinal regression, significantly reducing computation time by approximately 40% through parallel processing, and provides conditions for its optimality.

Contribution

It presents a novel parallelizable algorithm for optimal threshold labeling in ordinal regression and establishes conditions for its correctness.

Findings

Reduced computation time by about 40% with parallel processing.

Algorithm successfully finds optimal threshold labeling.

Conditions for the algorithm's success are derived.

Abstract

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.