Fast Best-Subset Selection Library
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Updated
Jun 16, 2024 - C++
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
A C++ cross-platform framework for machine learning algorithms development and testing.
Conditional Distance Correlation based Statistical Method
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
Machine learning library for classification tasks
ExhaustiveSearch: A Fast and Scalable Exhaustive Feature Selection Framework
R package CORElearn
MultiDimensional Feature Selection (MDFS) implementation for Python.
Predicting the regulatory role of CRP transcription factor in Escherichia coli
MultiDimensional Feature Selection (MDFS) implementation for R.
This is an implementation of Fuzzy Rough feature selection based on binary Shuffled Frog Leaping Algorithm
This is an implementation of Fuzzy Rough QuickReduct algorithm
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