Improved Co-training Semi-supervised SVM and Its Application in Oil Layer Recognition

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Improved Co-training Semi-supervised SVM and Its Application in Oil Layer Recognition

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[1]PAN Yongke,HE Ziping,XIA Kewen,et al.Improved Co-training Semi-supervised SVM and Its Application in Oil Layer Recognition[J].Journal of Zhengzhou University (Engineering Science),2022,43(01):14-19.[doi:10.13705/j.issn.1671-6833.2022.01.001]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 01 Page number: 14-19 Column: Public date: 2022-01-09

Title:: Improved Co-training Semi-supervised SVM and Its Application in Oil Layer Recognition

Author(s):: PAN Yongke; HE Ziping; XIA Kewen; NIU Wenjia; School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China

Keywords:: semi-supervised support vector machine; co-training; QPSO; data editing; oil layer recognition

CLC:: TP18

DOI:: 10.13705/j.issn.1671-6833.2022.01.001

Abstract:: It is expensive to obtain labeled data in actual oil logging, and a large amount of cheap unlabeled data are not used. How to use limited labeled samples and a large number of unlabeled samples to obtain accurate oil layer distribution remains to be solved. The semi-supervised learning methods were widely used because they could obtain good classification models using both a small number of labeled samples and a large number of unlabeled samples. Therefore, based on a semi-supervised support vector machine (S3VM), an improved semi-supervised support vector machine based on co-training and quantum-behaved particle swarm optimization algorithm (QPSO-CS3VM) was proposed for oil layer recognition. Firstly, the multi-view-based co-training strategy combined with S3VM was used to construct two independent initial classifiers, and then exchanged and labelled unlabeled samples to improve the overall oil layer recognition accuracy. Secondly, in order to improve the initial classification accuracy of original classifiers, the quantum behavioral particle swarm algorithm was introduced to optimize S3VM. Finally, a newly nearest neighbor data editing approach was used to predict the confidence of the pseudo-labelling of unlabeled data to reduce the deterioration of model perfor-mance caused by misclassification of data. The improved co-training semi-supervised SVM proposed in this paper improved the classification accuracy by 5.00% and 3.12% compared to the traditional co-training algorithm by performing oil layer recognition on the logging data of the two wells. The algorithm proposed in this paper had high accuracy in oil layer recognition and had practical application.

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Last Update: 2022-01-09