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AutoML for Multi-Label Classification: Overview and Empirical Evaluation
2021
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label)
doi:10.1109/tpami.2021.3051276
fatcat:4p3hvjf26ff4pozl4x626juqja