Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/6192
When is resampling beneficial for feature selection with imbalanced wide data?
Expert Systems with Applications. 2022, V. 188, 116015
Fecha de publicación
This paper studies the effects that combinations of balancing and feature selection techniques have on wide data (many more attributes than instances) when different classifiers are used. For this, an extensive study is done using 14 datasets, 3 balancing strategies, and 7 feature selection algorithms. The evaluation is carried out using 5 classification algorithms, analyzing the results for different percentages of selected features, and establishing the statistical significance using Bayesian tests. Some general conclusions of the study are that it is better to use RUS before the feature selection, while ROS and SMOTE offer better results when applied afterwards. Additionally, specific results are also obtained depending on the classifier used, for example, for Gaussian SVM the best performance is obtained when the feature selection is done with SVM-RFE before balancing the data with RUS.
High dimensional data
Very low sample size
Versión del editor
Documento(s) sujeto(s) a una licencia Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internacional