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:: Volume 5, Issue 1 (6-2017) ::
jgit 2017, 5(1): 133-152 Back to browse issues page
Support Vector Random Machines (SVRMs), A Optimum Multiclassifier for Big Data
Mohsen Jafari *, Mehdi Akhoundzadeh
University of Tehran
Abstract:   (2307 Views)

Although, the distinction between the land cover classes was increased in large feature space of remote sensing images, but
the low number of training data prevent this. In order to solve this problem, ensemble classification methods can be used
instead of individual classifiers. In this paper, a new method for ensemble support vector machine was proposed called
“Support Vector Random Machines (SVRMs(”. In proposed method, bootstrap was produced using modification of
training data and feature space. Simultaneous boosting SVM was used for basic classifiers. Then, classification map was
resulted using SVM fusion of basic classifier. Hyperspectral and Polarimetric SAR data was chosen for evaluation
performance of the SVRMs. Experiments were evaluated from three different points of view: First, evaluation against other
ensemble SVM methods; second, evaluation against various feature selection methods in classification and third,
evaluation against the various basic and new classification methods. As the results, proposed method is 16% better than the
individual SVM classifier in hyperspectral data and this is 10% in PolSAR data. Also, the classification results of SVRMs
in various classes compared to other SVM ensemble method were improved. The results reported from the proposed
method compared to the other feature selection method (Genetic Algorithm) has the effectual performance in classification.
The results show that the proposed method presents a better performance compared to the basic classification methods
(maximum likelihood and wishart) and advanced classification (random forest and neural network).

Keywords: Support Vector Machine (SVM), Ensemble method, Feature space, Bootstarp, Aggregation
Full-Text [PDF 1653 kb]   (1183 Downloads)    
Type of Study: Research | Subject: GIS
Received: 2017/06/10 | Accepted: 2017/06/10 | Published: 2017/06/10
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jafari M, Akhoundzadeh M. Support Vector Random Machines (SVRMs), A Optimum Multiclassifier for Big Data. jgit. 2017; 5 (1) :133-152
URL: http://jgit.kntu.ac.ir/article-1-428-en.html

Volume 5, Issue 1 (6-2017) Back to browse issues page
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
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