Jingwei Too

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.

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.

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

Binary-Particle-Swarm-Optimization-for-Feature-Selection. Simple algorithm shows how binary particle swarm optimization (BPSO) used in feature selection problem.

Salp-Swarm-Algorithm-for-Feature-Selection. Application of Salp Swarm Algorithm (SSA) in the 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.

Sine-Cosine-Algorithm-for-Feature-Selection. Application of Sine Cosine Algorithm (SCA) in the feature selection tasks.

Binary-Dragonfly-Algorithm-for-Feature-Selection. Application of Binary Dragonfly Algorithm (BDA) in the feature selection tasks.

Equilibrium-Optimizer-for-Feature-Selection. Application of Equilibrium Optimizer (EO) in the feature selection tasks.

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.

Genetic-Algorithm-for-Feature-Selection. Simple algorithm shows how the genetic algorithm (GA) used in the feature selection problem.

Atom-Search-Optimization-for-Feature-Selection. Application of Atom Search Optimization (ASO) in the feature selection tasks.

Deep-Learning-Toolbox-Python. This toolbox offers several deep learning methods, which are simple 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.

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.

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.

Machine-Learning-Regression-Toolbox. This toolbox offers 7 machine learning methods for regression problems.

Dimensionality-Reduction-Demonstration. Application of principal component analysis (PCA) for feature reduction.

JingweiToo.

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

Dash-by-Plotly. Interactive data analytics