Experiments showed that our method improves the overall quality of the qualification while also increasing the quality of the classification for smaller ...

This paper presents a novel method in which a label hierarchy structured as a directed acyclic graph (DAG) is created from the multi-label label space, taking ...

In this paper, We study the important problem of enforcing fairness on multi-label classification. Given the ubiquitous imbalanced issue with respect to label ...

Sep 23, 2021 · This paper proposes a fair classifier chain machine learning model for multi-label classification. Our algorithm solves the multi-label classification problem ...

Missing: Classification | Show results with:Classification

Feb 7, 2023 · We then propose a new framework named Similarity s-induced Fairness (Sγ-SimFair). This new framework utilizes data that have similar labels when ...

Abstract. Algorithmic Fairness is an established area of machine learning, willing to reduce the influence of hidden bias in the data.

Jun 27, 2023 · We formulate online hierarchical multi-labeled classification as an online optimization task that jointly learns individual label predictors and ...

Aug 1, 2024 · A novel evaluation approach is introduced in this research based on k-fold cross-validation and statistical t-tests to assess the fairness of ML algorithms.

However, the task becomes much more complex when a fair description of images is required, such as in multi-label classification tasks [82, 129, 134, 148] ...