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An Efficient Contesting Procedure for AutoML Optimization
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DOI：10.1109/ACCESS.2022.3192036.png)
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Abstract
En 中文
Automated Machine Learning (AutoML) frameworks are designed to select the optimal combination of operators and hyperparameters. Classical AutoML-based Bayesian Optimization approaches often integrate all operator search spaces into a single search space. However, a disadvantage of this history-based strategy is that it can be less robust when initialized randomly than optimizing each operator algorithm combination independently. To overcome this issue, a novel contesting procedure algorithm, Divide And Conquer Optimization (DACOpt), is proposed to make AutoML more robust. DACOpt partitions the AutoML search space into a reasonable number of sub-spaces based on algorithm similarity and budget constraints. Furthermore, throughout the optimization process, DACOpt allocates resources to each sub-space to ensure that (1) all areas of the search space are covered and (2) more resources are assigned to the most promising sub-space. Two extensive sets of experiments on 117 benchmark datasets demonstrate that DACOpt achieves significantly better results in 36% of AutoML benchmark datasets: 5% when to compared to TPOT, 8% - to AutoSklearn, 15% - to H20 and 18% - to ATM.
Keywords:
Optimization
Pipelines
Machine learning
Bayes methods
Classification algorithms
Search problems
Mathematical models
Automated machine learning (AutoML) optimization
machine learning
divide and conquer
Bayesian optimization
hyperparameter optimization
classification
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3.6
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9.8W
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29.4W
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