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- W2952074906 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." @default.
- W2952074906 created "2019-06-27" @default.
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- W2952074906 date "2018-01-01" @default.
- W2952074906 modified "2023-10-03" @default.
- W2952074906 title "ML-Plan for Unlimited-Length Machine Learning Pipelines" @default.
- W2952074906 hasPublicationYear "2018" @default.
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