Early Warning System for Seismic Events in Coal Mines Using Machine Learning

Autor:	Bogucki, Robert, Lasek, Jan, Milczek, Jan Kanty, Tadeusiak, Michal
Rok vydání:	2016
Předmět:	Computer Science - Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
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. Comment: Winner of AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1609.06957 Zobrazit plný text záznamu View this record from Arxiv