Feature Selection Library (FSLib 2018) is a widely applicable MATLAB library for feature selection.
Methods provided with FSLib:
If you use our toolbox (or method included in it), please consider to cite:
 Roffo, G., Melzi, S., Castellani, U. and Vinciarelli, A., 2017. Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach. arXiv preprint arXiv:1707.07538.
 Roffo, G., Melzi, S. and Cristani, M., 2015. Infinite feature selection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 4202-4210).
 Roffo, G. and Melzi, S., 2017, July. Ranking to learn: Feature ranking and selection via eigenvector centrality. In New Frontiers in Mining Complex Patterns: 5th International Workshop, NFMCP 2016, Held in Conjunction with ECML-PKDD 2016, Riva del Garda, Italy, September 19, 2016, Revised Selected Papers (Vol. 10312, p. 19). Springer.
 Roffo, G., 2017. Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications. arXiv preprint arXiv:1706.05933.
Giorgio (2020). Feature Selection Library (https://www.mathworks.com/matlabcentral/fileexchange/68210-feature-selection-library), MATLAB Central File Exchange. Retrieved .
Inspired by: Infinite Feature Selection
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