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oai:riubu.ubu.es:10259/48142022-04-29T12:02:45Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

00925njm 22002777a 4500 dc Kuncheva, Ludmila I. . author Rodríguez Diez, Juan José author 2018-09 High-dimensional data with very few instances are typical in many application domains. Selecting a highly discriminative subset of the original features is often the main interest of the end user. The widely-used feature selection protocol for such type of data consists of two steps. First, features are selected from the data (possibly through cross-validation), and, second, a cross-validation protocol is applied to test a classifier using the selected features. The selected feature set and the testing accuracy are then returned to the user. For the lack of a better option, the same low-sample-size dataset is used in both steps. Questioning the validity of this protocol, we carried out an experiment using 24 high-dimensional datasets, three feature selection methods and five classifier models. We found that the accuracy returned by the above protocol is heavily biased, and therefore propose an alternative protocol which avoids the contamination by including both steps in a single cross-validation loop. Statistical tests verify that the classification accuracy returned by the proper protocol is significantly closer to the true accuracy (estimated from an independent testing set) compared to that returned by the currently favoured protocol. 0031-3203 http://hdl.handle.net/10259/4814 10.1016/j.patcog.2018.03.012 Feature selection Wide datasets Experimental protoco Training/testing Cross-validation On feature selection protocols for very low-sample-size data