Application of Support Vector Machine Model in Mine Gas Safety Level Prediction

Huaping Zhou, Ruixin Zhang

Abstract


For the limitation of traditional information fusion technology in the mine gas safety class predicition, an intelligent algorithm is proposed in which Genetic Algorithms is adopted to optimize the parameters of the least squares support vector machine and establishes a multi-sensor information fusion model GA-LSSVM which overcomes the subjectivity and blindness on parameters selection, and thus improves its classification accuracy and convergence speed. Experimental results show that compared to the least squares support vector machine model not been optimized and the least squares support vector machine model optimized by the grid searching algorithm, GA-LSSVM model can be a good solution on the issue of the high-dimensional, nonlinear and uncertainty of the small sample in coal mine underground environment level evaluation.

 

DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4364


Keywords


Information fusion; genetic algorithms; least squares support vector machine; parameter optimization; cross validation

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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