Automatic feature engineering for prediction of dangerous seismic activities in coal mines

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. non-overlapping) 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.