Hybrid feature selection based on SLI and genetic algorithm for microarray datasets
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.
| Veröffentlicht in: | The Journal of supercomputing. - 1998. - 78(2022), 18 vom: 27., Seite 19725-19753 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | The Journal of supercomputing |
| Schlagworte: | Journal Article Genetic algorithm High-dimensional datasets Hybrid feature selection |
| Zusammenfassung: | © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. One of the major problems in microarray datasets is the large number of features, which causes the issue of "the curse of dimensionality" when machine learning is applied to these datasets. Feature selection refers to the process of finding optimal feature set by removing irrelevant and redundant features. It has a significant role in pattern recognition, classification, and machine learning. In this study, a new and efficient hybrid feature selection method, called Garank&rand, is presented. The method combines a wrapper feature selection algorithm based on the genetic algorithm (GA) with a proposed filter feature selection method, SLI-γ. In Garank&rand, some initial solutions are built regarding the most relevant features based on SLI-γ, and the remaining ones are only the random features. Eleven high-dimensional and standard datasets were used for the accuracy evaluation of the proposed SLI-γ. Additionally, four high-dimensional well-known datasets of microarray experiments were used to carry out an extensive experimental study for the performance evaluation of Garank&rand. This experimental analysis showed the robustness of the method as well as its ability to obtain highly accurate solutions at the earlier stages of the GA evolutionary process. Finally, the performance of Garank&rand was also compared to the results of GA to highlight its competitiveness and its ability to successfully reduce the original feature set size and execution time |
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| Beschreibung: | Date Revised 06.09.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0920-8542 |
| DOI: | 10.1007/s11227-022-04650-w |