使用监督模型对 SDSS-IV MaStar 光谱进行优化采样以用于恒星分类
Optimized sampling of SDSS-IV MaStar spectra for stellar classification using supervised models
AI总结：
本文研究利用主动学习从类别不平衡的 SDSS-IV MaStar 光谱数据中高效采样，以更少训练实例训练出优于随机和分层采样的恒星分类模型，并验证其在有效温度、表面重力和金属丰度预测中的有效性。
摘要：
监督机器学习模型正越来越多地被用于解决光谱数据的恒星分类问题。然而，训练此类模型需要大量带标签的实例，而收集这些实例通常在时间和专业知识方面成本高昂。主动学习算法通过仅保留信息量最大的实例来最小化训练数据集的规模。本文探讨了主动学习在利用来自高度类别不平衡数据集的数据进行恒星光谱采样中的应用。我们使用 SDSS DR17 中的 MaStar 库及其关联的恒星参数目录。对数据应用了包括特征选择、缩放和降维在内的预处理流程。使用不同的主动学习算法，我们迭代地查询实例，其中模型或模型委员会分别表现出最高的不确定性或分歧。我们通过比较在查询样本上训练的监督学习模型与随机采样对应模型的若干性能指标来评估采样技术的有效性。评估指标包括特异性、灵敏度和曲线下面积；此外还有 Matthew 相关系数，该系数考虑了类别不平衡。我们将此过程分别应用于有效温度、表面重力和铁金属丰度。我们的结果表明，主动学习算法在选择样本方面是有效的，这些样本产生的性能指标优于随机采样甚至分层采样，且训练实例更少。建议采用主动学习来优先安排天文巡天数据由专家或众包进行实例标注，以降低高昂的时间成本。其有效性还可以进一步用于在自动化天文巡天中为后续观测选择目标。
英文摘要：
Supervised machine learning models are increasingly being used for solving the problem of stellar classification of spectroscopic data. However, training such models requires a large number of labelled instances, the collection of which is usually costly in both time and expertise. Active learning algorithms minimize training dataset sizes by keeping only the most informative instances. This paper explores the application of active learning to sampling stellar spectra using data from a highly class-imbalanced dataset. We utilize the MaStar library from the SDSS DR17 along with its associated stellar parameter catalogue. A preprocessing pipeline that includes feature selection, scaling, and dimensionality reduction is applied to the data. Using different active learning algorithms, we iteratively query instances, where the model or committee of models exhibits the highest uncertainty or disagreement, respectively. We assess the effectiveness of the sampling techniques by comparing several performance metrics of supervised-learning models trained on the queried samples with randomly-sampled counterparts. Evaluation metrics include specificity, sensitivity, and the area under the curve; in addition to the Matthew's correlation coefficient, which accounts for class imbalance. We apply this procedure to effective temperature, surface gravity, and iron metallicity, separately. Our results demonstrate the effectiveness of active learning algorithms in selecting samples that produce performance metrics superior to random sampling and even stratified samples, with fewer training instances. Active learning is recommended for prioritizing instance labelling of astronomical-survey data by experts or crowdsourcing to mitigate the high time cost. Its effectiveness can be further exploited in selection of targets for follow-up observations in automated astronomical surveys.
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