Ensemble Squared: A Meta AutoML System

• URL: http://arxiv.org/abs/2012.05390v1
• Date: Thu, 10 Dec 2020 01:09:00 GMT
• Title: Ensemble Squared: A Meta AutoML System
• Authors: Jason Yoo, Tony Joseph, Dylan Yung, S. Ali Nasseri, Frank Wood
• Abstract summary: This paper presents a "meta" AutoML system that ensembles at the level of AutoML systems. Ensemble Squared exploits the diversity of existing, competing AutoML systems by ensembling the top-performing models simultaneously generated by a set of them.
• Score: 13.062016289815054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The continuing rise in the number of problems amenable to machine learning solutions, coupled with simultaneous growth in both computing power and variety of machine learning techniques has led to an explosion of interest in automated machine learning (AutoML). This paper presents Ensemble Squared (Ensemble$^2$), a "meta" AutoML system that ensembles at the level of AutoML systems. Ensemble$^2$ exploits the diversity of existing, competing AutoML systems by ensembling the top-performing models simultaneously generated by a set of them. Our work shows that diversity in AutoML systems is sufficient to justify ensembling at the AutoML system level. In demonstrating this, we also establish a new state of the art AutoML result on the OpenML classification challenge.

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