Low-cost scalable discretization, prediction and feature selection for complex systems Article Swipe
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· 2019
· Open Access
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· DOI: https://doi.org/10.1101/720441
Finding reliable discrete approximations of complex systems is a key prerequisite when applying many of the most popular modeling tools. Common discretization approaches (for example, the very popular K-means clustering) are crucially limited in terms of quality 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. Cross-validated applications of SPA to a range of large realistic data classification and prediction problems reveal drastic cost and performance improvements. For example, SPA allows the unsupervised next-day surface temperature predictions for Europe with the mean crossvalidated one-day 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 the next-day surface temperature predictions calculated on supercomputers and provided by the weather services). One Sentence Summary Introduced computational tool allows obtaining drastic cost and quality gains for a broad range of science applications.
Related Topics To Compare & Contrast
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/720441
- https://www.biorxiv.org/content/biorxiv/early/2019/07/31/720441.full.pdf
- OA Status
- green
- Cited By
- 4
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2965435989