Automatic feature engineering for prediction of dangerous seismic activities in coal mines
Date Issued
2016-09-11
Author(s)
Abstract
In this paper we present our submission to the
AAIA’16 Data Mining Challenge, where the objective was to
predict dangerous seismic events based on hourly aggregated
readings from different sensor and recent mining expert assessment of the conditions in the mine. During the course of
the competition we have exploited a framework for automatic
feature extraction from time series data that did not require
any manual tuning. Furthermore, we have analyzed the impact
of overlapping of input data on model robustness. We argue
that training an ensemble of classifiers with distinct (i.e. nonoverlapping) chronological data rather than one classifier with all
available data can produce more reliable and robust prediction
models. By doing that, we were able to avoid overfitting and
obtain the same score performance on the evaluation and test
datasets, despite the significant data drift in the datasets.
AAIA’16 Data Mining Challenge, where the objective was to
predict dangerous seismic events based on hourly aggregated
readings from different sensor and recent mining expert assessment of the conditions in the mine. During the course of
the competition we have exploited a framework for automatic
feature extraction from time series data that did not require
any manual tuning. Furthermore, we have analyzed the impact
of overlapping of input data on model robustness. We argue
that training an ensemble of classifiers with distinct (i.e. nonoverlapping) chronological data rather than one classifier with all
available data can produce more reliable and robust prediction
models. By doing that, we were able to avoid overfitting and
obtain the same score performance on the evaluation and test
datasets, despite the significant data drift in the datasets.
Subjects
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