CAST: Cluster-Aware Self-Training for Tabular Data

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 …

algorithms architecture cluster confidence cs.lg data domains issue labels self-training simplicity solutions tabular tabular data training vulnerable