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New PUe Framework Enhances Learning with Biased Datasets · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework called PUe to enhance Positive-Unlabeled (PU) learning by addressing selection bias in real-world datasets. This framework, building on prior work by Bekker et al., introduces a normalized inverse-probability-weighted PU risk formulation and integrates with modern cost-sensitive methods. Experiments on datasets like MNIST, CIFAR-10, and ADNI show PUe outperforms existing PU baselines when label distributions are uneven. AI

IMPACT This research could improve the accuracy of machine learning models trained on real-world data that often suffers from biased labeling.

RANK_REASON The cluster contains a research paper detailing a new machine learning framework.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New PUe Framework Enhances Learning with Biased Datasets · 2 sources tracked

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Xutao Wang, Hanting Chen, Tianyu Guo, Yunhe Wang ·

    PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference

    arXiv:2607.13428v1 Announce Type: new Abstract: Positive-Unlabeled (PU) learning aims to achieve high-accuracy binary classification with limited labeled positive examples and numerous unlabeled ones. Existing cost-sensitive-based methods often rely on strong assumptions that exa…

  2. arXiv cs.LG TIER_1 English(EN) · Yunhe Wang ·

    PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference

    Positive-Unlabeled (PU) learning aims to achieve high-accuracy binary classification with limited labeled positive examples and numerous unlabeled ones. Existing cost-sensitive-based methods often rely on strong assumptions that examples with an observed positive label were selec…

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