Fast Best-Subset Selection Library

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

Python3 binding to mRMR Feature Selection algorithm (currently not maintained)

Efficient Algorithms for L0 Regularized Learning

An improved implementation of the classical feature selection method: minimum Redundancy and Maximum Relevance (mRMR).

An in-depth tutorial on the theory of panorama stitching

Best Subset Selection algorithm for Regression, Classification, Count, Survival analysis

C++ implementation of the GMS Feature Correspondence Algorithm

Conditional Distance Correlation based Statistical Method

A C++ cross-platform framework for machine learning algorithms development and testing.

Machine learning library for classification tasks

An implementation of the Feature Selection with Annealing method from: Barbu et al. Feature Selection with Annealing for Computer Vision and Big Data Learning. IEEE PAMI, 39, No. 2, 272–286, 2017 https://arxiv.org/abs/1310.2880

ExhaustiveSearch: A Fast and Scalable Exhaustive Feature Selection Framework

Predicting the regulatory role of CRP transcription factor in Escherichia coli

R package CORElearn

MultiDimensional Feature Selection (MDFS) implementation for R.

MultiDimensional Feature Selection (MDFS) implementation for Python.

Causal Feature Selection With Dual Correction

This is an implementation of Fuzzy Rough feature selection based on binary Shuffled Frog Leaping Algorithm