Abstract
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%.
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Fernandez-Lozano, C., Seoane, J.A., Gestal, M., Gaunt, T.R., Campbell, C. (2013). Texture Classification Using Kernel-Based Techniques. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_42
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DOI: https://doi.org/10.1007/978-3-642-38679-4_42
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