Feature Selection Library

版本 7.0.2020.3 (723.3 KB) 作者: Giorgio
Feature Selection Library (MATLAB Toolbox)
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更新时间 2020/5/3

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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.
We would greatly appreciate it if you kindly give us some feedback on this toolbox. We value your opinion and welcome your rating.
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.

引用格式

Giorgio (2024). Feature Selection Library (https://www.mathworks.com/matlabcentral/fileexchange/56937-feature-selection-library), MATLAB Central File Exchange. 检索时间: .

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FSLib_v7.0.1_2020_2

FSLib_v7.0.1_2020_2/eval_metrics

FSLib_v7.0.1_2020_2/lib

FSLib_v7.0.1_2020_2/lib/@algorithm

FSLib_v7.0.1_2020_2/lib/@data

FSLib_v7.0.1_2020_2/lib/@distance

FSLib_v7.0.1_2020_2/lib/@fisher

FSLib_v7.0.1_2020_2/lib/@kernel

FSLib_v7.0.1_2020_2/lib/@l0

FSLib_v7.0.1_2020_2/lib/@loss

FSLib_v7.0.1_2020_2/lib/@rfe

FSLib_v7.0.1_2020_2/lib/@svm

FSLib_v7.0.1_2020_2/lib/drtoolbox

FSLib_v7.0.1_2020_2/lib/drtoolbox/gui

FSLib_v7.0.1_2020_2/lib/drtoolbox/techniques

FSLib_v7.0.1_2020_2/lib/files

FSLib_v7.0.1_2020_2/methods

版本 已发布 发行说明
7.0.2020.3

Typos
7.0.2020.2

Updated demo file: Demo_InfFS.m
% To run this code you need to complete it.
% This file is not ready to run. you can use part of it.
% You need to add your dataset and install LIBLINEAR SVM classifier
7.0.2020.1

From Brais Cancela comments some updates have been done on ILFS method.
IMPORTANT NOTE:
The implementation of PLSA + EM algorithm was based on the code at:
https://github.com/lizhangzhan/plsa
https://github.com/lizhangzhan/plsa/blob/master/plsa.m
6.2.2018.1

+ Add method: infFS_fast
6.2.2018.0

+ New Methods:
[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
[16] fsasl
[17] dgufs
[18] ufsol
[19] lasso

6.1.2018.0

+ Added new Demo file: how to select the best parameters for the Inf-FS and ILFS.
+ How to obtain the best results with the Inf-FS approach.

6.0.2018.0

+ File separator for current platform included.