Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization

• URL: http://arxiv.org/abs/2107.01253v1
• Date: Fri, 2 Jul 2021 20:06:40 GMT
• Title: Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization
• Authors: Paulito P. Palmes, Akihiro Kishimoto, Radu Marinescu, Parikshit Ram, Elizabeth Daly
• Abstract summary: We create the AMLP toolkit which facilitates the creation and evaluation of complex machine learning pipeline structures. 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.
• Score: 18.82755278152806
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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