Texture Classification Using Kernel-Based Techniques

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• 作者：Carlos Fernandez-Lozano (19)
  Jose A. Seoane (20)
  Marcos Gestal (19)
  Tom R. Gaunt (20)
  Colin Campbell (21)
  
• 关键词：Multiple Kernel Learning ; Support Vector Machines ; Recursive Feature Elimination ; Genetic Algorithms
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：7902
• 期：1
• 页码：435-444
• 全文大小：140KB
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• 作者单位：Carlos Fernandez-Lozano (19)
  Jose A. Seoane (20)
  Marcos Gestal (19)
  Tom R. Gaunt (20)
  Colin Campbell (21)
  
  19. Information and Communications Technologies Department, Faculty of Computer Science, University of A Coru帽a, Campus Elvi帽a s/n, 15071, A Coru帽a, Spain
  20. MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS82BN, UK
  21. Department of Engineering Mathematics, University of Bristol, Merchant Venturer鈥檚 Building, Bristol, BS81UB, UK
  
• ISSN：1611-3349

文摘

In this paper, a high-dimensional textural heterogenous dataset is evaluated. This problem should be studied with specific techniques or a solution for decreasing dimensionality should be applied in order to improve the classification results. Thus, this problem is tackled by means of three differente techniques: an specific technique such as Multiple Kernel Learning, and two different feature selection techniques such as Support Vector Machines-Recursive Feature Elimination and a Genetic Algorithm-based approaches. We found that the best technique is Support Vector Machines-Recursive Feature Elimination, with a AUROC score of 92,45%.