Infinite Feature Selection
InfFS allows you to rank a huge list of feature, even more than 40000 features and 10000 samples.
The Inf-FS is a graph-based method which exploits the convergence properties of the power series of matrices to evaluate the importance of a feature with respect to all the other ones taken together. Indeed, in the Inf-FS formulation, each feature is mapped on an affinity graph, where nodes represent features and weighted edges relationships between them. Each path of a certain length l over the graph is seen as a possible selection of features. Therefore, varying these paths and letting them tend to an infinite number permits the investigation of the importance of each possible subset of features. The Inf-FS assigns a final score to each feature of the initial set; where the score is related to how much the given feature is a good candidate regarding the classification task. Therefore, ranking in descendant order the outcome of the Inf-FS allows us to perform the subset feature selection throughout a model selection stage to determine the number of features to be selected.
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Reference : Infinite Feature Selection
Link Paper :http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7410835
ResearchGate: https://www.researchgate.net/publication/282576688_Infinite_Feature_Selection
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Updates
| 4.2 | + Infinite Feature Selection Dec. 2016: "Unsupervised" & "Supervised" versions. |
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| 4.0 | New methods
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| 3.0 | - Added new method: Features Selection via Eigenvector Centrality (ECFS) 2016
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| 2.2 | - New Inf-FS
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| 1.6 | - some problems fixed |
MATLAB Release
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Acknowledgements
Inspired: Feature Selection by Eigenvector Centrality, Feature Selection Library
ruqiya (view profile)