Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Fisher’s Linear Discriminant Analysis Based Prediction using Transient Features of Seismic Events in Coal Mines

,

DOI: http://dx.doi.org/10.15439/2016F116

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 231–234 ()

Full text

Abstract. Identification of seismic activity levels in coal mines is important to avoid accidents such as rockburst. Creating an early warning system that can save lives requires an automated way of predicting. This study proposes a prediction algorithm for the AAIA016 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines that is based on transient activity features along with average indicators evaluated by a Fisher's linear discriminant analysis. Performance evaluation experiments on the training datasets revealed an accuracy level of around 0.9438 while the performance on the test dataset was at a level of 0.9297. These results suggest that the proposed approach achieves high accuracy in predicting danger seismic events while maintaining low complexity.

References

  1. A. Janusz, M. Sikora, Ł. Wróbel, S. Stawicki, M. Grzegorowski, P. Wojtas, and D. Ślęzak, “Mining data from coal mines: Ijcrs’15 data challenge,” in Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Springer, 2015, pp. 429–438.
  2. M. Sari, H. S. B. Duzgun, C. Karpuz, and A. S. Selcuk, “Accident analysis of two turkish underground coal mines,” Safety Science, vol. 42, no. 8, pp. 675–690, 2004. http://dx.doi.org/10.1016/j.ssci.2003.11.002
  3. J. Van Zyl and C. W. Omlin, “Prediction of seismic events in mines using neural networks,” in Neural Networks, 2001. Proceedings. IJCNN’01. International Joint Conference on, vol. 2. IEEE, 2001. http://dx.doi.org/10.1109/IJCNN.2001.939568 pp. 1410–1414.
  4. J. V. Tu, “Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes,” Journal of clinical epidemiology, vol. 49, no. 11, pp. 1225–1231, 1996. http://dx.doi.org/http://dx.doi.org/10.1016/S0895-4356(96)00002-9
  5. R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of eugenics, vol. 7, no. 2, pp. 179–188, 1936. http://dx.doi.org/10.1111/j.1469-1809.1936.tb02137.x
  6. B. Bagwell, “A journey through flow cytometric immunofluorescence analyses—finding accurate and robust algorithms that estimate positive fraction distributions,” Clinical Immunology Newsletter, vol. 16, no. 3, pp. 33–37, 1996. http://dx.doi.org/10.1016/S0197-1859(00)80002-3
  7. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification. John Wiley & Sons, 2012.