Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups

• Main Authors: Dehghankar, Mohsen, Asudeh, Abolfazl
• Format: Journal Article
• Language: English
• Published: 07-11-2024
• Subjects: Computer Science - Learning
• DOI: 10.48550/arxiv.2411.04761

Due to a variety of reasons, such as privacy, data in the wild often misses
the grouping information required for identifying minorities. On the other
hand, it is known that machine learning models are only as good as the data
they are trained on and, hence, may underperform for the under-represented
minority groups. The missing grouping information presents a dilemma for
responsible data scientists who find themselves in an unknown-unknown
situation, where not only do they not have access to the grouping attributes
but do not also know what groups to consider.
This paper is an attempt to address this dilemma. Specifically, we propose a
minority mining problem, where we find vectors in the attribute space that
reveal potential groups that are under-represented and under-performing.
Technically speaking, we propose a geometric transformation of data into a dual
space and use notions such as the arrangement of hyperplanes to design an
efficient algorithm for the problem in lower dimensions. Generalizing our
solution to the higher dimensions is cursed by dimensionality. Therefore, we
propose a solution based on smart exploration of the search space for such
cases. We conduct comprehensive experiments using real-world and synthetic
datasets alongside the theoretical analysis. Our experiment results demonstrate
the effectiveness of our proposed solutions in mining the unknown,
under-represented, and under-performing minorities.