Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have been proposed and many fairness-aware methods are developed. However, most of existing definitions and methods focus only on single-label classification. Fairness for multi-label classification, where each instance is associated with more than one labels, is still yet to establish. To fill this gap, we study fairness-aware multi-label classification in this paper. We start by extending Demographic Parity (DP) and Equalized Opportunity (EOp), two popular fairness notions, to multi-label classification scenarios. Through a systematic study, we show that on multi-label data, because of unevenly distributed labels, EOp usually fails to construct a reliable estimate on labels with few instances. We then propose a new framework named Similarity s-induced Fairness (sγ -SimFair). This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp. Theoretical analysis and experimental results on real-world datasets together demonstrate the advantage of sγ -SimFair over existing methods on multi-label classification tasks.
This content will become publicly available on December 15, 2024
Online Hierarchical Multi-label Classification
Existing approaches for multi-label classification are trained offline, missing the opportunity to adapt to new data instances as they become available. To address this gap, an online multi-label classification method was proposed recently, to learn from data instances sequentially. In this work, we focus on multi-label classification tasks, in which the labels are organized in a hierarchy. We formulate online hierarchical multi-labeled classification as an online optimization task that jointly learns individual label predictors and a label threshold, and propose a novel hierarchy constraint to penalize predictions that are inconsistent with the label hierarchy structure. Experimental results on three benchmark datasets show that the proposed approach outperforms online multi-label classification methods, and achieves comparable to, or even better performance than offline hierarchical classification frameworks with respect to hierarchical evaluation metrics.
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- Award ID(s):
- 1737443
- NSF-PAR ID:
- 10507193
- Publisher / Repository:
- IEEE International Conference on Big Data
- Date Published:
- ISBN:
- 979-8-3503-2445-7
- Page Range / eLocation ID:
- 5346-5355
- Subject(s) / Keyword(s):
- Hierarchical classification online learning learning with constrains
- Format(s):
- Medium: X
- Location:
- Sorrento, Italy
- Sponsoring Org:
- National Science Foundation
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