Texture classification using feature selection and kernel-based techniques
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605468729 | ||
| 003 | CHVBK | ||
| 005 | 20210128100318.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150901xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00500-014-1573-5 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1573-5 | ||
| 245 | 0 | 0 | |a Texture classification using feature selection and kernel-based techniques |h [Elektronische Daten] |c [Carlos Fernandez-Lozano, Jose Seoane, Marcos Gestal, Tom Gaunt, Julian Dorado, Colin Campbell] |
| 520 | 3 | |a 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 ( $$95.88 \pm 0.39\,\%$$ 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. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2015 | ||
| 690 | 7 | |a Multiple kernel learning |2 nationallicence | |
| 690 | 7 | |a Support vector machines |2 nationallicence | |
| 690 | 7 | |a Feature selection |2 nationallicence | |
| 690 | 7 | |a Texture analysis |2 nationallicence | |
| 690 | 7 | |a Recursive feature elimination |2 nationallicence | |
| 700 | 1 | |a Fernandez-Lozano |D Carlos |u Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain |4 aut | |
| 700 | 1 | |a Seoane |D Jose |u Bristol Genetic Epidemiology Laboratories, School of Social and Community Medicine, University of Bristol, Oakfield House, BS82BN, Oakfield Grove, Bristol, UK |4 aut | |
| 700 | 1 | |a Gestal |D Marcos |u Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain |4 aut | |
| 700 | 1 | |a Gaunt |D Tom |u MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Oakfield House, BS82BN, Oakfield Grove, Bristol, UK |4 aut | |
| 700 | 1 | |a Dorado |D Julian |u Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain |4 aut | |
| 700 | 1 | |a Campbell |D Colin |u Intelligent Systems Laboratory, University of Bristol, Merchant Venturer's Building, BS81UB, Bristol, UK |4 aut | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/9(2015-09-01), 2469-2480 |x 1432-7643 |q 19:9<2469 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1573-5 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1573-5 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Fernandez-Lozano |D Carlos |u Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Seoane |D Jose |u Bristol Genetic Epidemiology Laboratories, School of Social and Community Medicine, University of Bristol, Oakfield House, BS82BN, Oakfield Grove, Bristol, UK |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Gestal |D Marcos |u Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Gaunt |D Tom |u MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Oakfield House, BS82BN, Oakfield Grove, Bristol, UK |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Dorado |D Julian |u Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Campbell |D Colin |u Intelligent Systems Laboratory, University of Bristol, Merchant Venturer's Building, BS81UB, Bristol, UK |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/9(2015-09-01), 2469-2480 |x 1432-7643 |q 19:9<2469 |1 2015 |2 19 |o 500 | ||