Low-cost scalable discretization, prediction and feature selection for complex systems Article Swipe
Susanne Gerber
,
Lukáš Pospíšil
,
Mohit Navandar
,
Illia Horenko
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1101/720441
YOU?
·
· 2019
· Open Access
·
· 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
Concepts
Discretization
Scalability
Computer science
Range (aeronautics)
Probabilistic logic
Feature selection
Cluster analysis
Feature (linguistics)
Data mining
Key (lock)
Quality (philosophy)
Machine learning
Selection (genetic algorithm)
Artificial intelligence
Mathematical optimization
Mathematics
Mathematical analysis
Materials science
Linguistics
Database
Composite material
Epistemology
Computer security
Philosophy
Metadata
- 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
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2965435989Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/720441Digital Object Identifier
- Title
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Low-cost scalable discretization, prediction and feature selection for complex systemsWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-07-31Full publication date if available
- Authors
-
Susanne Gerber, Lukáš Pospíšil, Mohit Navandar, Illia HorenkoList of authors in order
- Landing page
-
https://doi.org/10.1101/720441Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2019/07/31/720441.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2019/07/31/720441.full.pdfDirect OA link when available
- Concepts
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Discretization, Scalability, Computer science, Range (aeronautics), Probabilistic logic, Feature selection, Cluster analysis, Feature (linguistics), Data mining, Key (lock), Quality (philosophy), Machine learning, Selection (genetic algorithm), Artificial intelligence, Mathematical optimization, Mathematics, Mathematical analysis, Materials science, Linguistics, Database, Composite material, Epistemology, Computer security, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2022: 1, 2020: 1Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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