Feature Selection Library

Feature Selection Library (MATLAB Toolbox)

Feature Selection Library (FSLib 2018) is a widely applicable MATLAB library for feature selection.
Methods provided with FSLib:
[1] ILFS
[2] InfFS
[3] ECFS
[4] mrmr
[5] relieff
[6] mutinffs
[7] fsv
[8] laplacian
[9] mcfs
[10] rfe
[11] L0
[12] fisher
[13] UDFS
[14] llcfs
[15] cfs

If you use our toolbox (or method included in it), please consider to cite:
[1] 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.
[2] Roffo, G., Melzi, S. and Cristani, M., 2015. Infinite feature selection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 4202-4210).
[3] 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.
[4] Roffo, G., 2017. Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications. arXiv preprint arXiv:1706.05933.