When is resampling beneficial for feature selection with imbalanced wide data?
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 algorith...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Burgos (UBU) |
| Repositorio: | Repositorio Institucional de la Universidad de Burgos (RIUBU) |
| OAI Identifier: | oai:riubu.ubu.es:10259/7326 |
| Acceso en línea: | http://hdl.handle.net/10259/7326 |
| Access Level: | acceso abierto |
| Palabra clave: | Feature selection Wide data High dimensional data Very low sample size Unbalanced Machine learning Informática Computer science |
| Sumario: | 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. |
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