Gerber, S. and Pospisil, L. and Navandar, M. and Horenko, I.
(2020)
*Low-cost scalable discretization, prediction and feature selection for complex systems.*
Science Advances, 6
(5).

Full text not available from this repository.

Official URL: https://advances.sciencemag.org/content/6/5/eaaw09...

## Abstract

Finding reliable discrete approximations of complex systems is a key prerequisite when applying many of the most popular modeling tools. Common discretization approaches (e.g., the very popular K-means clustering) are crucially limited in terms of quality, parallelizability, and cost. We introduce a low-cost improved quality scalable probabilistic approximation (SPA) algorithm, allowing for simultaneous data-driven optimal discretization, feature selection, and prediction. We prove its optimality, parallel efficiency, and a linear scalability of iteration cost. Cross-validated applications of SPA to a range of large realistic data classification and prediction problems reveal marked cost and performance improvements. For example, SPA allows the data-driven next-day predictions of resimulated surface temperatures for Europe with the mean prediction error of 0.75°C on a common PC (being around 40% better in terms of errors and five to six orders of magnitude cheaper than with common computational instruments used by the weather services).

Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences > Mathematics > Applied Mathematics |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics |

ID Code: | 2558 |

Deposited By: | Monika Drueck |

Deposited On: | 27 Apr 2021 12:47 |

Last Modified: | 27 Apr 2021 12:47 |

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