Clustering Based Sampling for Learning from Unbalanced Seismic Data Set

Abstract

This article describes how some stratum contain a stress concentration zones, and while the stress increases and exceeds a high value or so called critical value, it destroys rocks. This causes the emission of seismic tremors of different energies. Seismology consists of the study of the effects of seismic waves, and predicting the seismic hazards to rocks and long wall coals. This is alongside the main problem occurred in this field, the unbalanced data that lacks cause when studying the seismic hazards. Learning from unbalanced data is considered as one of the most difficult issues to solve nowadays, this article presents an informed sampling method that is based on a clustering approach for the prediction of seismic hazards in Polish coal mines. The idea is based on the dividing of non-hazardous examples which represents more than 90% of the real-life cases into subsets of examples in order to balance the classes. This action facilitates the learning from the recorded data. For evaluation, the authors have evaluated the system based on the prediction of seismic hazards where positive results have been reviewed compared to the classification of examples without balancing the cases.