Texture classification using feature selection and kernel-based techniques

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 ± 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.