Validity and efficiency of conformal anomaly detection on big distributed data
| Autor: | Ilia Nouretdinov |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Physics and Astronomy (miscellaneous)
lcsh:T business.industry Computer science Conformal anomaly Pattern recognition Anomaly detection computer.software_genre lcsh:Technology Distributed computing Management of Technology and Innovation lcsh:Q Conformal prediction Artificial intelligence Data mining lcsh:Science business Engineering (miscellaneous) computer |
| Zdroj: | Advances in Science, Technology and Engineering Systems Journal Advances in Science, Technology and Engineering Systems, Vol 2, Iss 3, Pp 254-267 (2017) |
| ISSN: | 2415-6698 |
| DOI: | 10.25046/aj020335 |
| Popis: | Conformal Prediction is a recently developed framework for reliable confident predictions. In this work we discuss its possible application to big data coming from different, possibly heterogeneous data sources. On example of anomaly detection problem, we study the question of saving validity of Conformal Prediction in this case. We show that the straight forward averaging approach is invalid, while its easy alternative of maximizing is not very efficient because of its conservativeness. We propose the third compromised approach that is valid, but much less conservative. It is supported by both theoretical justification and experimental results in the area of energy engineering. |
| Databáze: | OpenAIRE |
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