Rough Set-based Feature Selection with MATLAB Implementation
MATLAB source code for implementing feature selection algorithms based on rough set theory, including core data structures and attribute reduction functions.
MATLAB Implementation of Rough Set Algorithms for Data Reduction
Data Reduction Techniques Using Fuzzy Rough Sets and Fuzzy Mutual Information with Implementation Approaches - Feature Evaluation and Selection Based on Fuzzy Preference Rough Set Methods
MATLAB Implementation of Feature Selection Methods
Feature selection methods with MATLAB implementations including l-add r-remove algorithm, Sequential Floating Forward Selection (SFFS), sequential backward selection, and sequential forward selection with code implementation details
Particle Swarm Optimization for Feature Selection and SVM Parameter Tuning
Implementing feature selection and SVM parameter optimization using particle swarm optimization algorithm with code implementation insights
Feature Selection Combining PCA and ICA
Feature selection by combining PCA and ICA: performing principal component analysis first, followed by independent component analysis on the resulting features
Machine Learning Source Code for Dimensionality Reduction, Feature Selection, and Related Algorithms
Comprehensive machine learning source code authored by Zhejiang University professors Cai Deng and He Xiaofei, covering spectral regression, dimensionality reduction, feature selection, topic modeling, matrix factorization, sparse coding, hashing techniques, clustering methods, active learning, and matrix learning. This collection serves as an excellent resource for understanding algorithm implementations through practical code examples.
MATLAB Machine Learning Code Implementation
Comprehensive MATLAB source code for machine learning, featuring multi-class SVM algorithms, pattern recognition systems, feature selection methods, and various regression techniques with practical implementation examples.
MATLAB Function for Feature Selection Using Particle Swarm Optimization (PSO)
This MATLAB function implements Particle Swarm Optimization (PSO) for feature selection, offering customizable optimization direction, population size, iteration count, and other parameters with detailed algorithm implementation insights.
MATLAB Code Implementation for Principal Component Analysis (PCA)
This code implements Principal Component Analysis (PCA) for feature selection, extracting the top three principal components with the highest variance contribution
Novel Swarm Intelligence Algorithm with Exceptional Optimization Capabilities
A groundbreaking bee colony optimization algorithm featuring superior performance, applicable to feature selection and various computational domains with efficient implementation approaches