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ctlab/ITMO_FS

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ITMO_FS

Feature selection library in Python

Package information: Python 2.7 Python 3.6 License Docs CodeCov

Install with

pip install ITMO_FS

Current available algorithms:

Supervised filters Unsupervised filters Wrappers Hybrid Embedded Ensembles
Spearman correlation Trace Ratio (Laplacian) Add Del Filter Wrapper MOSNS MeLiF
Pearson correlation Multi-Cluster Feature Selection Backward selection IWSSr-SFLA MOSS Best goes first
Fit Criterion Unsupervised Discriminative Feature Selection Sequential Forward Selection   RFE Best sum
F ratio   QPFS      
Gini index   Hill climbing      
Symmetric Uncertainty   Simulated Annealing      
Fechner correlation   Recursive Elimination      
Kendall correlation          
Information Gain          
ANOVA          
Chi-squared          
Relief          
ReliefF          
Laplacian score          
Modified T-score          
Mutual Information Maximization          
Minimum Redundancy Maximum Relevance          
Joint Mutual Information          
Conditional Infomax Feature Extraction          
Mutual Information Feature Selection          
Conditional Mutual Info Maximization          
Interaction Capping          
Dynamic Change of Selected Feature          
Composition of Feature Relevancy          
Max-Relevance and Max-Independence          
Interaction Weight          
Double Input Symmetric Relevance          
Fast Correlation          
Statistical Inference Relief          
Trace Ratio (Fisher)          
Nonnegative Discriminative Feature Selection          
Robust Feature Selection          
Spectral Feature Selection          
VDM          
QPFS          
MIMAGA          

Documentation:

https://itmo-fs.readthedocs.io/en/latest/