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Wrapper-Feature-Selection-Toolbox-Python
This toolbox offers 13 wrapper feature selection methods (PSO, GA, GWO, HHO, BA, WOA, and etc.) with examples. It is simple and easy to implement.Wrapper-Feature-Selection-Toolbox
This toolbox offers more than 40 wrapper feature selection methods include PSO, GA, DE, ACO, GSA, and etc. They are simple and easy to implement.EMG-Feature-Extraction-Toolbox
This toolbox offers 40 feature extraction methods (EMAV, EWL, MAV, WL, SSC, ZC, and etc.) for Electromyography (EMG) signals applications.EEG-Feature-Extraction-Toolbox
This toolbox offers 30 types of EEG feature extraction methods (HA, HM, HC, and etc.) for Electroencephalogram (EEG) applications.Binary-Grey-Wolf-Optimization-for-Feature-Selection
Demonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.Advanced-Feature-Selection-Toolbox
This toolbox offers advanced feature selection tools. Several modifications, variants, enhancements, or improvements of algorithms such as GWO, FPA, SCA, PSO and SSA are provided.Whale-Optimization-Algorithm-for-Feature-Selection
Application of Whale Optimization Algorithm (WOA) in the feature selection tasks.Machine-Learning-Toolbox
This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.Filter-Feature-Selection-Toolbox
Simple, fast and ease of implementation. The filter feature selection methods include Relief-F, PCC, TV, and NCA.Binary-Harris-Hawk-Optimization-for-Feature-Selection
The binary version of Harris Hawk Optimization (HHO), called Binary Harris Hawk Optimization (BHHO) is applied for feature selection tasks.Ant-Colony-Optimization-for-Feature-Selection
Implantation of ant colony optimization (ACO) without predetermined number of selected features in feature selection tasks.Binary-Differential-Evolution-for-Feature-Selection
The binary version of Differential Evolution (DE), named as Binary Differential Evolution (BDE) is applied for feature selection tasks.Sine-Cosine-Algorithm-for-Feature-Selection
Application of Sine Cosine Algorithm (SCA) in the feature selection tasks.Neural-Network-Toolbox
This toolbox contains 6 types of neural networks, which is simple and easy to implement.Salp-Swarm-Algorithm-for-Feature-Selection
Application of Salp Swarm Algorithm (SSA) in the feature selection tasks.Equilibrium-Optimizer-for-Feature-Selection
Application of Equilibrium Optimizer (EO) in the feature selection tasks.Binary-Particle-Swarm-Optimization-for-Feature-Selection
Simple algorithm shows how binary particle swarm optimization (BPSO) used in feature selection problem.Particle-Swarm-Optimization-for-Feature-Selection
Application of Particle Swarm Optimization (PSO) in the feature selection tasks.Henry-Gas-Solubility-Optimization-for-Feature-Selection
Application of Henry Gas Solubility Optimization (HGSO) in the feature selection tasks.Binary-Dragonfly-Algorithm-for-Feature-Selection
Application of Binary Dragonfly Algorithm (BDA) in the feature selection tasks.Genetic-Algorithm-for-Feature-Selection
Simple algorithm shows how the genetic algorithm (GA) used in the feature selection problem.Ant-Colony-System-for-Feature-Selection
Application of ant colony optimization (ACO) for feature selection problems.Deep-Learning-Toolbox-Python
This toolbox offers several deep learning methods, which are simple and easy to implement.Binary-Tree-Growth-Algorithm-for-Feature-Selection
A feature selection algorithm, named as Binary Tree Growth Algorithm (BTGA) is applied for feature selection tasks.Atom-Search-Optimization-for-Feature-Selection
Application of Atom Search Optimization (ASO) in the feature selection tasks.Deep-Learning-Toolbox
This toolbox offers convolution neural networks (CNN) using k-fold cross-validation, which are simple and easy to implement.Machine-Learning-Regression-Toolbox
This toolbox offers 7 machine learning methods for regression problems.Machine-Learning-Toolbox-Python
This toolbox offers 6 machine learning methods including KNN, SVM, LDA, DT, and etc., which are simpler and easy to implement.JingweiToo
Dimensionality-Reduction-Demonstration
Application of principal component analysis (PCA) for feature reduction.Love Open Source and this site? Check out how you can help us