Texture classification using feature selection and kernel-based techniques

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• 作者：Carlos Fernandez-Lozano ; Jose A. Seoane ; Marcos Gestal ; Tom R. Gaunt…
• 关键词：Multiple kernel learning ; Support vector machines ; Feature selection ; Texture analysis ; Recursive feature elimination
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：19
• 期：9
• 页码：2469-2480
• 全文大小：773 KB
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• 作者单位：Carlos Fernandez-Lozano (1)
  Jose A. Seoane (2) (3)
  Marcos Gestal (1)
  Tom R. Gaunt (4)
  Julian Dorado (1)
  Colin Campbell (5)
  
  1. 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
  2. Bristol Genetic Epidemiology Laboratories, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS82BN, UK
  3. Stanford Cancer Institute, Stanford School of Medicine, Stanford University, 780 Welch Road, Office CJ220, Palo Alto, CA, 94304, USA
  4. MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS82BN, UK
  5. Intelligent Systems Laboratory, University of Bristol, Merchant Venturer鈥檚 Building, Bristol, BS81UB, UK
  
• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

The interpretation of the results in a classification problem can be enhanced, specially in image texture analysis problems, by feature selection techniques, knowing which features contribute more to the classification performance. This paper presents an evaluation of a number of feature selection techniques for classification in a biomedical image texture dataset (2-DE gel images), with the aim of studying their performance and the stability in the selection of the features. We analyse three different techniques: subgroup-based multiple kernel learning (MKL), which can perform a feature selection by down-weighting or eliminating subsets of features which shares similar characteristic, and two different conventional feature selection techniques such as recursive feature elimination (RFE), with different classifiers (naive Bayes, support vector machines, bagged trees, random forest and linear discriminant analysis), and a genetic algorithm-based approach with an SVM as decision function. The different classifiers were compared using a ten times tenfold cross-validation model, and the best technique found is SVM-RFE, with an AUROC score of (\(95.88 \pm 0.39\,\%\)). However, this method is not significantly better than RFE-TREE, RFE-RF and grouped MKL, whilst MKL uses lower number of features, increasing the interpretability of the results. MKL selects always the same features, related to wavelet-based textures, while RFE methods focuses specially co-occurrence matrix-based features, but with high instability in the number of features selected.