Ranking hierarchical multi-label classification results with mLPRs

• URL: http://arxiv.org/abs/2205.07833v2
• Date: Sun, 02 Nov 2025 08:51:23 GMT
• Title: Ranking hierarchical multi-label classification results with mLPRs
• Authors: Yuting Ye, Christine Ho, Ci-Ren Jiang, Wayne Tai Lee, Haiyan Huang,
• Abstract summary: We focus on the less attended second-stage question while adhering to the given class hierarchy.<n>We introduce a new objective function, called CATCH, to ensure reasonable classification performance.<n>Our approach was evaluated on a synthetic dataset and two real datasets.
• Score: 4.869182515096001
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hierarchical multi-label classification (HMC) has gained considerable attention in recent decades. A seminal line of HMC research addresses the problem in two stages: first, training individual classifiers for each class, then integrating these classifiers to provide a unified set of classification results across classes while respecting the given hierarchy. In this article, we focus on the less attended second-stage question while adhering to the given class hierarchy. This involves addressing a key challenge: how to manage the hierarchical constraint and account for statistical differences in the first-stage classifier scores across different classes to make classification decisions that are optimal under a justifiable criterion. To address this challenge, we introduce a new objective function, called CATCH, to ensure reasonable classification performance. To optimize this function, we propose a decision strategy built on a novel metric, the multidimensional Local Precision Rate (mLPR), which reflects the membership chance of an object in a class given all classifier scores and the class hierarchy. Particularly, we demonstrate that, under certain conditions, transforming the classifier scores into mLPRs and comparing mLPR values for all objects against all classes can, in theory, ensure the class hierarchy and maximize CATCH. In practice, we propose an algorithm HierRank to rank estimated mLPRs under the hierarchical constraint, leading to a ranking that maximizes an empirical version of CATCH. Our approach was evaluated on a synthetic dataset and two real datasets, exhibiting superior performance compared to several state-of-the-art methods in terms of improved decision accuracy.

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