应用于SDSS星系的机器学习红移叠加
Stacking for machine learning redshifts applied to SDSS galaxies
摘要：
我们提出了一个分析的一般机器学习技术称为“堆叠”的光度红移的估计。堆叠技术可以将作为基础算法输出的光度红移估计反馈回相同的算法，作为后续学习回合中的附加输入特征。我们展示了所有测试的基本算法如何从至少一个额外的堆叠轮（或层）中受益。为了证明堆叠的好处，我们将该方法应用于基于自组织映射（SOM）的无监督机器学习技术和基于决策树的有监督机器学习方法。我们探索了一系列堆叠架构，例如层数和每层的基本学习器数量。最后，我们探讨的有效性堆叠，即使使用一个成功的算法，如AdaBoost。当堆栈应用于弱学习器（如SOM和决策树）时，我们观察到所有计算指标的显着改善在1.9%到21%之间。当应用于强学习算法（如AdaBoost）时，改进的比例缩小，但仍然保持为正，并且对于探索的指标在0.4%到2.5%之间，并且几乎没有额外的计算成本。
英文摘要：
We present an analysis of a general machine learning technique called 'stacking' for the estimation of photometric redshifts. Stacking techniques can feed the photometric redshift estimate, as output by a base algorithm, back into the same algorithm as an additional input feature in a subsequent learning round. We shown how all tested base algorithms benefit from at least one additional stacking round (or layer). To demonstrate the benefit of stacking, we apply the method to both unsupervised machine learning techniques based on self-organising maps (SOMs), and supervised machine learning methods based on decision trees. We explore a range of stacking architectures, such as the number of layers and the number of base learners per layer. Finally we explore the effectiveness of stacking even when using a successful algorithm such as AdaBoost. We observe a significant improvement of between 1.9% and 21% on all computed metrics when stacking is applied to weak learners (such as SOMs and decision trees). When applied to strong learning algorithms (such as AdaBoost) the ratio of improvement shrinks, but still remains positive and is between 0.4% and 2.5% for the explored metrics and comes at almost no additional computational cost.
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