Recognition of Oil Shale Based on LIBSVM Optimized by Modified Genetic Algorithm
Qihua Hu1, *, Cong Wang2, Xin Zhang1, Jingjing Fan1
Identifiers and Pagination:Year: 2015
First Page: 363
Last Page: 367
Publisher Id: TOPEJ-8-363
Article History:Received Date: 10/4/2015
Revision Received Date: 20/5/2015
Acceptance Date: 15/6/2015
Electronic publication date: 19/8/2015
Collection year: 2015
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In order to improved the speed, accuracy and generalization of oil shale recognition model with log dada, considering parameters of traditional SVM were chosen by experience, a LIBSVM recognition model with optimized parameters was proposed based genetic algorithm. First of all, all the samples data were processed to double type as LIBSVM tool needing, and the best normalization way was chosen through comparing different accuracies of various normalization ways. Secondly, the fitness value was calculated by the traditional LIBSVM. Finally, parameters C and g were optimized by genetic algorithm according the fitness value. The optimized LIBSVM oil shale recognition model was applied in northern Qaidam basin to identify oil shale, the results show that optimized recognition model is a tool of better generalization ability and the recognition accuracy reaches as much as 97.2806%. According to the popularization effects in the well area of same geology background, this optimized LIBSVM model is the best for now.