@article{21004,
  abstract     = {{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) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.}},
  author       = {{Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}},
  issn         = {{0162-8828}},
  journal      = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
  keywords     = {{Automated Machine Learning, Multi Label Classification, Hierarchical Planning, Bayesian Optimization}},
  pages        = {{1--1}},
  title        = {{{AutoML for Multi-Label Classification: Overview and Empirical Evaluation}}},
  doi          = {{10.1109/tpami.2021.3051276}},
  year         = {{2021}},
}

@inproceedings{3852,
  abstract     = {{In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated.
Various AutoML tools have already been introduced to provide out-of-the-box machine learning functionality.
More specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand.
Except for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline.
However, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets.
In this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length.
We evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.}},
  author       = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}},
  booktitle    = {{ICML 2018 AutoML Workshop}},
  keywords     = {{automated machine learning, complex pipelines, hierarchical planning}},
  location     = {{Stockholm, Sweden}},
  title        = {{{ML-Plan for Unlimited-Length Machine Learning Pipelines}}},
  year         = {{2018}},
}