ITMO_FS

Feature selection library in Python

Package information:

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/