Feature selection library in Python
Package information: 
Install with
pip install ITMO_FSCurrent 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: