Jingwei Too's repositories
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.
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.
Binary-Grey-Wolf-Optimization-for-Feature-Selection
Demonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.
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.
Whale-Optimization-Algorithm-for-Feature-Selection
Application of Whale Optimization Algorithm (WOA) in the 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-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.
Neural-Network-Toolbox
This toolbox contains 6 types of neural networks, which is simple and easy to implement.
Sine-Cosine-Algorithm-for-Feature-Selection
Application of Sine Cosine Algorithm (SCA) in the feature selection tasks.
Particle-Swarm-Optimization-for-Feature-Selection
Application of Particle Swarm Optimization (PSO) in the feature selection tasks.
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-Dragonfly-Algorithm-for-Feature-Selection
Application of Binary Dragonfly Algorithm (BDA) in the feature selection tasks.
Deep-Learning-Toolbox-Python
This toolbox offers several deep learning methods, which are simple and easy to implement.
Henry-Gas-Solubility-Optimization-for-Feature-Selection
Application of Henry Gas Solubility Optimization (HGSO) in the feature selection tasks.
Ant-Colony-System-for-Feature-Selection
Application of ant colony optimization (ACO) for feature selection problems.
Binary-Tree-Growth-Algorithm-for-Feature-Selection
A feature selection algorithm, named as Binary Tree Growth Algorithm (BTGA) is applied for 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.
Genetic-Algorithm-for-Feature-Selection
Simple algorithm shows how the genetic algorithm (GA) used in the feature selection problem.
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.
Dimensionality-Reduction-Demonstration
Application of principal component analysis (PCA) for feature reduction.
Dash-by-Plotly
Interactive data analytics
reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.