CAST: Cluster-Aware Self-Training for Tabular Data

• URL: http://arxiv.org/abs/2310.06380v2
• Date: Fri, 2 Feb 2024 17:31:05 GMT
• Title: CAST: Cluster-Aware Self-Training for Tabular Data
• Authors: Minwook Kim, Juseong Kim, Ki Beom Kim, Giltae Song
• Abstract summary: Self-training is vulnerable to noisy pseudo-labels caused by erroneous confidence. Cluster-Aware Self-Training (CAST) enhances existing self-training algorithms at a negligible cost without significant modifications.
• Score: 0.5461938536945723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-training has gained attraction because of its simplicity and versatility, yet it is vulnerable to noisy pseudo-labels caused by erroneous confidence. Several solutions have been proposed to handle the problem, but they require significant modifications in self-training algorithms or model architecture, and most have limited applicability in tabular domains. To address this issue, we explore a novel direction of reliable confidence in self-training contexts and conclude that the confidence, which represents the value of the pseudo-label, should be aware of the cluster assumption. In this regard, we propose Cluster-Aware Self-Training (CAST) for tabular data, which enhances existing self-training algorithms at a negligible cost without significant modifications. Concretely, CAST regularizes the confidence of the classifier by leveraging local density for each class in the labeled training data, forcing the pseudo-labels in low-density regions to have lower confidence. Extensive empirical evaluations on up to 21 real-world datasets confirm not only the superior performance of CAST but also its robustness in various setups in self-training contexts.

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