Granular approach to data processing under probabilistic uncertainty
| Autor: | Vladik Kreinovich, Andrzej Pownuk |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Data processing Speedup Computer science Computation Granular computing Probabilistic logic Process (computing) Computational intelligence 02 engineering and technology Computer Science Applications 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Granularity Algorithm Information Systems |
| Zdroj: | Granular Computing. 6:489-505 |
| ISSN: | 2364-4974 2364-4966 |
| DOI: | 10.1007/s41066-019-00210-5 |
| Popis: | The existing algorithms for data processing under probabilistic uncertainty often require too much computation time. Sometimes, we can speed up the corresponding computations if we take into account the fact that in many real-life situations, uncertainty can be naturally described as a combination of several components, components which are described by different granules. In such situations, to process this uncertainty, it is often beneficial to take this granularity into account by processing these granules separately and then combining the results. In this paper, we show that granular computing can help even in situations when there is no such natural decomposition into granules, namely we can often speed up processing of uncertainty if we first (artificially) decompose the original uncertainty into appropriate granules. |
| Databáze: | OpenAIRE |
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