CSForest introduces a novel approach to address discrepancies between training and test sets, enhancing accuracy in outlier detection. By leveraging unlabeled test samples, CSForest constructs high-quality prediction sets with true label coverage guarantees. Extensive experiments demonstrate CSForest's superior performance in inlier classification and outlier detection compared to alternative methods.
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by Yujin Han,Mi... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2302.02237.pdfDeeper Inquiries