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- W4381377894 abstract "The pipeline optimization problem in machine learning requires simultaneous optimization of pipeline structures and parameter adaptation of their elements. Having an elegant way to express these structures can help lessen the complexity in the management and analysis of their performances together with the different choices of optimization strategies. With these issues in mind, we created the AutoMLPipeline (AMLP) toolkit which facilitates the creation and evaluation of complex machine learning pipeline structures using simple expressions. We use AMLP to find optimal pipeline signatures, datamine them, and use these datamined features to speed-up learning and prediction. We formulated a two-stage pipeline optimization with surrogate modeling in AMLP which outperforms other AutoML approaches with a 4-hour time budget in less than 5 minutes of AMLP computation time." @default.
- W4381377894 created "2023-06-21" @default.
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- W4381377894 date "2021-07-02" @default.
- W4381377894 modified "2023-10-06" @default.
- W4381377894 title "Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization" @default.
- W4381377894 doi "https://doi.org/10.48550/arxiv.2107.01253" @default.
- W4381377894 hasPublicationYear "2021" @default.
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