Feature Selection Library (FSLib 2018) is a widely applicable MATLAB library for feature selection (attribute or variable selection), capable of reducing the problem of high dimensionality to maximize the accuracy of data models, the performance of automatic decision rules as well as to reduce data acquisition cost.
* FSLib was awarded by MATLAB in 2017 by receiving a MATLAB Central Coin.
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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 (2021). Feature Selection Library (https://www.mathworks.com/matlabcentral/fileexchange/56937-feature-selection-library), MATLAB Central File Exchange. Retrieved .
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