Feature Selection Library
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.
Cite As
Giorgio (2024). Feature Selection Library (https://www.mathworks.com/matlabcentral/fileexchange/68210-feature-selection-library), MATLAB Central File Exchange. Retrieved .
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Acknowledgements
Inspired by: Infinite Feature Selection
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FSLib_v6.1_2018
FSLib_v6.1_2018/eval_metrics
FSLib_v6.1_2018/lib
FSLib_v6.1_2018/lib/@algorithm
FSLib_v6.1_2018/lib/@data
FSLib_v6.1_2018/lib/@distance
FSLib_v6.1_2018/lib/@fisher
FSLib_v6.1_2018/lib/@kernel
FSLib_v6.1_2018/lib/@l0
FSLib_v6.1_2018/lib/@loss
FSLib_v6.1_2018/lib/@rfe
FSLib_v6.1_2018/lib/@svm
FSLib_v6.1_2018/lib/drtoolbox
FSLib_v6.1_2018/lib/drtoolbox/gui
FSLib_v6.1_2018/lib/drtoolbox/techniques
FSLib_v6.1_2018/methods
Version | Published | Release Notes | |
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6.1.2018 |
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