Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost
Keywords: AutoML, Ensemble Selection, Hardware Aware, Multi-Objective Optimization
TL;DR: The proposed hardware-aware ensemble selection approach for AutoML integrates hardware constraints to balance predictive accuracy and operational efficiency, improving the deployment of resource-efficient machine learning models.
Abstract: Automated Machine Learning (AutoML) significantly simplifies the deployment of machine learning models by automating tasks from data preprocessing to model selection to ensembling. AutoML systems for tabular data often employ post hoc ensembling, where multiple models are combined to improve predictive accuracy. This typically results in longer inference times, a major limitation in practical deployments. Addressing this, we introduce a hardware-aware ensemble selection approach that integrates inference time into post hoc ensembling. By leveraging an existing framework for ensemble selection with quality diversity optimization, our method evaluates ensemble candidates for their predictive accuracy and hardware efficiency. This dual focus allows for a balanced consideration of accuracy and operational efficiency. Thus, our approach enables practitioners to choose from a Pareto front of accurate and efficient ensembles. Our evaluation using 83 classification datasets shows that our approach sustains competitive accuracy and can significantly improve ensembles' operational efficiency. The results of this study provide a foundation for extending these principles to additional hardware constraints, setting the stage for the development of more resource-efficient AutoML systems.
Submission Checklist: Yes
Broader Impact Statement: Yes
Paper Availability And License: Yes
Code Of Conduct: Yes
Optional Meta-Data For Green-AutoML: All questions below on environmental impact are optional.
Steps For Environmental Footprint Reduction During Development: We developed on a toy context and only used the larger context for evaluation.
CPU Hours: 144
GPU Hours: 0
TPU Hours: 0
Evaluation Metrics: No
Estimated CO2e Footprint: 2
Submission Number: 11
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