Repository: Freie Universität Berlin, Math Department

Low-cost scalable discretization, prediction and feature selection for complex systems

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).

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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
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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