JingweiToo/Machine-Learning-Regression-Toolbox

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2
Language
Python
License
BSD 3-Clause "New...
Created over 3 years ago
Updated over 3 years ago

JingweiToo/Machine-Learning-Regression-Toolbox

JingweiToo

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This toolbox offers 7 machine learning methods for regression problems.

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.

Atom-Search-Optimization-for-Feature-Selection

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

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-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.