arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新，休息一下吧，周末愉快~~
arXiv 2602.23666astro-ph.EPastro-ph.IMcs.LG
收藏

在极端类别不平衡下用于行星可居住性分类的主动学习

Active Learning for Planet Habitability Classification under Extreme Class Imbalance

Meteorology Faculty of Science, Cairo University（科学学院气象学系，开罗大学）

R. I. El-Kholy, Z. M. Hayman

备注：
20 pages, 9 figures, 2 tables
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出使用主动学习方法在极端类别不平衡条件下提高系外行星可居住性分类效率，通过优化召回率和减少标注需求，支持保守的优先级研究。

摘要：

随着系外行星目录的规模和异质性不断增加，系统性可居住性评估变得具有挑战性，特别是由于潜在可居住行星的极端稀缺性和其标签的演变性质。在本研究中，我们探讨了使用池式主动学习来提高在现实观测约束下可居住性分类的效率。我们从可居住世界目录和NASA系外行星档案构建了一个统一的数据集，并将可居住性评估制定为一个二元分类问题。建立了一个基于梯度提升决策树的监督基线，并对其进行优化以提高召回率，以便优先识别稀有的潜在可居住行星。然后将该模型嵌入到主动学习框架中，其中基于不确定性的边缘采样与随机查询在多个运行和标注预算下进行比较。我们发现主动学习显著减少了所需标记实例的数量，以接近监督性能，展示了明显的标签效率提升。为了将这些结果与实际天文用例联系起来，我们将独立训练的主动学习模型的预测汇总成一个集成模型，并使用由此产生的平均概率和不确定性对最初被标记为不可居住的行星进行排名。该过程识别出一个稳健的候选者供进一步研究，展示了主动学习如何支持保守、基于不确定性的优先级研究目标，而不是推测性重新分类。我们的结果表明，主动学习为在标签不平衡、信息不完整和观测资源有限的数据领域中指导可居住性研究提供了系统性的框架。

英文摘要：

The increasing size and heterogeneity of exoplanet catalogs have made systematic habitability assessment challenging, particularly given the extreme scarcity of potentially habitable planets and the evolving nature of their labels. In this study, we explore the use of pool-based active learning to improve the efficiency of habitability classification under realistic observational constraints. We construct a unified dataset from the Habitable World Catalog and the NASA Exoplanet Archive and formulate habitability assessment as a binary classification problem. A supervised baseline based on gradient-boosted decision trees is established and optimized for recall in order to prioritize the identification of rare potentially habitable planets. This model is then embedded within an active learning framework, where uncertainty-based margin sampling is compared against random querying across multiple runs and labeling budgets. We find that active learning substantially reduces the number of labeled instances required to approach supervised performance, demonstrating clear gains in label efficiency. To connect these results to a practical astronomical use case, we aggregate predictions from independently trained active-learning models into an ensemble and use the resulting mean probabilities and uncertainties to rank planets originally labeled as non-habitable. This procedure identifies a single robust candidate for further study, illustrating how active learning can support conservative, uncertainty-aware prioritization of follow-up targets rather than speculative reclassification. Our results indicate that active learning provides a principled framework for guiding habitability studies in data regimes characterized by label imbalance, incomplete information, and limited observational resources.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑