Hyperparameter Optimization of Topological Features for Machine Learning Applications
| Autor: | Franco Marinozzi, Hugh K. Haddox, Marcio Gameiro, Hamed Eramian, Christopher J. Tralie, Jed Singer, Steve Haase, Nicholas Leiby, Gilberto Bini, Devin Strickland, Rossella Bedini, Fabiano Bini, Gabe Rocklin, John Harer, Scott Novotney, Matt Vaughn, Francis C. Motta |
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| Rok vydání: | 2019 |
| Předmět: |
Hyperparameter
business.industry Computer science 010102 general mathematics Bayesian optimization 010501 environmental sciences Machine learning computer.software_genre Topology 01 natural sciences Pipeline (software) Hyperparameter optimization Vectorization (mathematics) Topological data analysis Point (geometry) Configuration space Artificial intelligence 0101 mathematics business computer 0105 earth and related environmental sciences |
| Zdroj: | ICMLA |
| DOI: | 10.1109/icmla.2019.00185 |
| Popis: | This paper describes a general pipeline for generating optimal vector representations of topological features of data for use with machine learning algorithms. This pipeline can be viewed as a costly black-box function defined over a complex configuration space, each point of which specifies both how features are generated and how predictive models are trained on those features. We propose using state-of-the-art Bayesian optimization algorithms to inform the choice of topological vectorization hyperparameters while simultaneously choosing learning model parameters. We demonstrate the need for and effectiveness of this pipeline using two difficult biological learning problems, and illustrate the nontrivial interactions between topological feature generation and learning model hyperparameters. |
| Databáze: | OpenAIRE |
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