Early Warning System for Seismic Events in Coal Mines Using Machine Learning
| Autor: | Michal Tadeusiak, Jan Kanty Milczek, Robert Bogucki, Jan Lasek |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
021110 strategic defence & security studies lcsh:T58.5-58.64 lcsh:Information technology business.industry Computer science 0211 other engineering and technologies Coal mining Machine Learning (stat.ML) 02 engineering and technology Machine learning computer.software_genre lcsh:QA75.5-76.95 Machine Learning (cs.LG) Computer Science - Learning Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Early warning system 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Artificial intelligence business computer |
| Zdroj: | FedCSIS Annals of computer science and information systems, Vol 8, Pp 213-220 (2016) |
| Popis: | This document describes an approach to the problem of predicting dangerous seismic events in active coal mines up to 8 hours in advance. It was developed as a part of the AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines. The solutions presented consist of ensembles of various predictive models trained on different sets of features. The best one achieved a winning score of 0.939 AUC. Winner of AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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