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.

ant-colony-optimization-for-feature-selection

Implantation of ant colony optimization (ACO) without predetermined number of selected features in feature selection tasks.

ant-colony-system-for-feature-selection

Application of ant colony optimization (ACO) for feature selection problems.

atom-search-optimization-for-feature-selection

Application of Atom Search Optimization (ASO) in the feature selection tasks.

binary-atom-search-optimization-for-feature-selection

A new feature selection algorithm, named as Binary Atom Search Optimization (BASO) is applied for 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.

binary-dragonfly-algorithm-for-feature-selection

Application of Binary Dragonfly Algorithm (BDA) in the feature selection tasks.

binary-grey-wolf-optimization-for-feature-selection

Demonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.

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.

binary-particle-swarm-optimization-for-feature-selection

Simple algorithm shows how binary particle swarm optimization (BPSO) used in feature selection problem.

binary-tree-growth-algorithm-for-feature-selection

A feature selection algorithm, named as Binary Tree Growth Algorithm (BTGA) is applied for feature selection tasks.

dash-by-plotly

Interactive data analytics

deep-learning-toolbox

This toolbox offers convolution neural networks (CNN) using k-fold cross-validation, which are simple and easy to implement.

deep-learning-toolbox-python

This toolbox offers several deep learning methods, which are simple and easy to implement.

dimensionality-reduction-demonstration

Application of principal component analysis (PCA) for feature reduction.

eeg-feature-extraction-toolbox

This toolbox offers 30 types of EEG feature extraction methods (HA, HM, HC, and etc.) for Electroencephalogram (EEG) applications.

emg-feature-extraction-toolbox

This toolbox offers 40 feature extraction methods (EMAV, EWL, MAV, WL, SSC, ZC, and etc.) for Electromyography (EMG) signals applications.

equilibrium-optimizer-for-feature-selection

Application of Equilibrium Optimizer (EO) in the feature selection tasks.

filter-feature-selection-toolbox

Simple, fast and ease of implementation. The filter feature selection methods include Relief-F, PCC, TV, and NCA.

genetic-algorithm-for-feature-selection

Simple algorithm shows how the genetic algorithm (GA) used in the feature selection problem.

henry-gas-solubility-optimization-for-feature-selection

Application of Henry Gas Solubility Optimization (HGSO) in the feature selection tasks.

jingweitoo

machine-learning-regression-toolbox

This toolbox offers 7 machine learning methods for regression problems.

machine-learning-toolbox

This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.

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.

neural-network-toolbox

This toolbox contains 6 types of neural networks, which is simple and easy to implement.

particle-swarm-optimization-for-feature-selection

Application of Particle Swarm Optimization (PSO) in the feature selection tasks.

reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

salp-swarm-algorithm-for-feature-selection

Application of Salp Swarm Algorithm (SSA) in the feature selection tasks.

sine-cosine-algorithm-for-feature-selection

Application of Sine Cosine Algorithm (SCA) in the feature selection tasks.