Abstract
A misclassification rate is most often used as a feature selection criterion. However, in the cases, when the numerical force of the training set is not sufficiently large in relation to the number of features, the risk of choosing the noisy features is very high. It produces difference between error estimations derived on the basis of the training and testing sets, so the error rate estimation can not be sufficiently confident. Feature preselection based on analysis of dependences between features is recommended in such types of tasks. An advantage of this approach is shown in the paper. As a feature selection criterion the Pearson chi-square statistics has been used.
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Kosla, P. (2007). A Feature Selection Approach in Problems with a Great Number of Features. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds) Computer Recognition Systems 2. Advances in Soft Computing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75175-5_50
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DOI: https://doi.org/10.1007/978-3-540-75175-5_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75174-8
Online ISBN: 978-3-540-75175-5
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