Automating Biomedical Data Science Through Tree-Based Pipeline Optimization

By Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, Jason H. Moore. 2016

336 citations recorded by the source corpus.

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning—pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators—such as synthetic feature constructors—that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Towards modular and programmable architecture search (2019, background): “Systems such as Auto-Sklearn [19], TPOT [20], and Auto-Keras [21] have been developed for specific use-cases (e.”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “For example, genetic programming could create a pipeline for a classification task without any classification algorithm (Olson et al., 2016).”
Survey on Automated Machine Learning (2019, methodology): “For example TPOT could create a pipeline for a classification task without any classification algorithm (Olson et al., 2016a).”
Deep Learning in the Wild (2018, background): “Recent research in automated machine learning [17,40] and respective academic challenges [22] accurately aimed at finding a solution to this problem for sets of practically relevant use cases.”
Autostacker: a compositional evolutionary learning system (2018, methodology): “The results yielded by Autostacker are further compared to those produced by the TPOT [21] model, one of the more recent and highly popular AutoML systems, as well as AutoSklearn [7], which won 1st place in the final phase of 2016 AutoML challenge.”
Towards human-guided machine learning (2019, methodology): “TPOT offers an alternative approach using genetic algorithms [3].”
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science (2016, methodology): “In this paper, we report on the most recent development of an evolutionary algorithm called the Tree-based Pipeline Optimization Tool (TPOT) that automatically designs and optimizes machine learning pipelines [13].”