Filter-Wrapper Combination and Embedded Feature Selection for Gene Expression Data
Published in International Journal of Advances in Soft Computing and its Applications, 2018
Recommended citation: Hameed, S. S., Petinrin, O. O., Hashi, A. O., & Saeed, F. (2018). "Filter-wrapper combination and embedded feature selection for gene expression data". Int. J. Advance Soft Compu. Appl, 10(1), 90-105.
This study utilised Filter-Wrapper combination and embedded (LASSO) feature selection methods on both high and low dimensional datasets before classification was performed. The results illustrate that the combination of filter and wrapper feature selection to create a hybrid form of feature selection provides better performance than using filter only. In addition, LASSO performed better on high dimensional data.
Recommended citation: Hameed, S. S., Petinrin, O. O., Hashi, A. O., & Saeed, F. (2018). “Filter-wrapper combination and embedded feature selection for gene expression data”. Int. J. Advance Soft Compu. Appl, 10(1), 90-105..