AUC Maximization from Biased Positive-unlabeled Data with Confidence
Updated 41 min ago · first seen 11 Sept 2026
paper_01M294FNWAYAHA9K04GN69TR3N
- Published
- 11 Sept 2026
- T1 · 41 min ago
- arXiv
- 2609.10928
- T1 · 41 min ago
- Category
- cs.LG
- T1 · 41 min ago
Abstract
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced binary classification. Although positive and negative data are required for maximizing the AUC, negative data are often difficult to collect in some real-world applications due to privacy concerns or the need for specialized expertise to annotate them. Thus, AUC maximization from positive and unlabeled (PU) data has been attracting attention. Existing methods assume that labeled positive data are unbiased samples from the true positive distribution. However, this ideal assumption is often violated in practice. In this paper, we propose a method to maximize the AUC from biased PU data. To address the bias, our key idea is to exploit {\it confidence}, i.e., the probability that an instance is positive, associated with the small number of labeled positive data. We derive an estimator of the AUC risk using biased PU data with confidence, enabling AUC maximization under such bias. We further show that the rewritten AUC risk induces a Bayes-optimal AUC ranking even when the available confidence is any strictly increasing transformation of the true posterior probability. We experimentally show the effectiveness of our method on eight real-world datasets.
Authors 6
Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Kazuki Adachi, Yasuhiro Fujiwara
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Arxiv announce type
- new
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- arXiv id
- 2609.10928
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Categories
- cs.LG, cs.AI, stat.ML
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Primary category
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 41 min agohigh
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9
Source tiers
T19
Freshest observation
41 min ago
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced binary classification. Although positive and negative data are required for maximizing the AUC, negative data are often difficult to collect in some real-world applications due to privacy concerns or the need for specialized expertise to annotate them. Thus, AUC maximization from positive and unlabeled (PU) data has been attracting attention. Existing methods assume that labeled positive data are unbiased samples from the true positive distribution. However, this ideal assumption is often violated in practice. In this paper, we propose a method to maximize the AUC from biased PU data. To address the bias, our key idea is to exploit {\it confidence}, i.e., the probability that an instance is positive, associated with the small number of labeled positive data. We derive an estimator of the AUC risk using biased PU data with confidence, enabling AUC maximization under such bias. We further show that the rewritten AUC risk induces a Bayes-optimal AUC ranking even when the available confidence is any strictly increasing transformation of the true posterior probability. We experimentally show the effectiveness of our method on eight real-world datasets. | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: AUC Maximization from Biased Positive-unlabeled Data with Confidence
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 41 min ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.