Conformal-in-the-Loop for Learning with Imbalanced Noisy Data

• URL: http://arxiv.org/abs/2411.02281v1
• Date: Mon, 04 Nov 2024 17:09:58 GMT
• Title: Conformal-in-the-Loop for Learning with Imbalanced Noisy Data
• Authors: John Brandon Graham-Knight, Jamil Fayyad, Nourhan Bayasi, Patricia Lasserre, Homayoun Najjaran,
• Abstract summary: Class imbalance and label noise are pervasive in large-scale datasets. Much of machine learning research assumes well-labeled, balanced data, which rarely reflects real world conditions. We propose Conformal-in-the-Loop (CitL), a novel training framework that addresses both challenges with a conformal prediction-based approach.
• Score: 5.69777817429044
• License:
• Abstract: Class imbalance and label noise are pervasive in large-scale datasets, yet much of machine learning research assumes well-labeled, balanced data, which rarely reflects real world conditions. Existing approaches typically address either label noise or class imbalance in isolation, leading to suboptimal results when both issues coexist. In this work, we propose Conformal-in-the-Loop (CitL), a novel training framework that addresses both challenges with a conformal prediction-based approach. CitL evaluates sample uncertainty to adjust weights and prune unreliable examples, enhancing model resilience and accuracy with minimal computational cost. Our extensive experiments include a detailed analysis showing how CitL effectively emphasizes impactful data in noisy, imbalanced datasets. Our results show that CitL consistently boosts model performance, achieving up to a 6.1% increase in classification accuracy and a 5.0 mIoU improvement in segmentation. Our code is publicly available: CitL.

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