![docs/logos/logo_itmo_fs_itog_colour.jpg](https://raw.githubusercontent.com/ctlab/ITMO_FS/master/docs/logos/logo_itmo_fs_itog_colour.jpg)
Feature selection library in Python
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: