Abstract:
While Machine Learning has achieved considerable success in recent years,
this success crucially relies on human experts to select appropriate
features, workflows, algorithms with their hyper-parameters, etc. Automating
the role of the human expert has seen some attention from the Machine
Learning community, with a dedicated workshop running since 2014 at one of
the top Machine Learning conferences.
In this work, we propose to exploit multiple AI Planning tools for
automating the human expert, generating Machine Learning pipelines
automatically. We start from a knowledge about possible valid pipelines
encoded as context-free grammar, translate the problem of generating the
corresponding language into Hierarchical Task Network (HTN) Planning model,
further translate the HTN Planning model into a classical planning model. We
use existing planners to produce multiple plans for the classical planning
task, translate these plans into Machine Learning pipelines, train and
evaluate these pipelines. Based on pipelines' accuracy feedback we update
the classical planning model to improve the quality of pipelines obtained in
next iterations. Using planning tools allows us to exploit the flexibility
of model update instead of solution modification. We present an application
that helps users to focus pipelines' exploration process by allowing to
encode additional constraints on desired pipelines. Our experimental
evaluation shows the feasibility of using planning techniques in this
context.
