Utilizing an ensemble of SVMs with GMM voting-based mechanism in predicting dangerous seismic events in active coal mines

This paper presents an application of a Gaussian Mixture Model-based voting mechanism for an ensemble of Support Vector Machines (SVMs) to the problem of predicting dangerous seismic events in active coal mines. The author proposes a method of preparing an ensemble of SVMs with different parameters and using the “wisdom of the crowd” for a classification problem. Experiments performed during the research showed an improvement in the quality of the classification after the mixture of Gaussian distributions was applied as votes distribution. The author also proposes a method of data selection for long sequences of measurement arranged chronologically with highly unbalanced occurrence of the positive class in the two-class classification problem. Finally, using the proposed model to solve the problem defined by the organizers of AAIA'16 DM showed an increase in the stability of the ensemble classifier and an improvement in the quality of the classification problem solution.