Fundamental Rough Set Algorithms
Core rough set algorithms including data completion, attribute reduction, value reduction, and rule generation, with practical implementation insights and code-related applications.
MATLAB Implementation Divided into Two Parts: PCA with Rough Sets and Fuzzy Neural Networks
This program consists of two main implementations: Part 1 combines PCA with Rough Sets and Fuzzy Neural Networks for face recognition, while Part 2 integrates PCA, LDA, Rough Sets and Fuzzy Neural Networks for pattern recognition. The implementation includes ORL face database handling, experimental results, and demonstrates practical approaches for dimensionality reduction and classification algorithms.
A Simple Introduction to Rough Set Theory
A beginner's guide to rough set theory with applications in pattern recognition and attribute reduction.
MATLAB Source Code for Data Reduction Algorithms in Rough Set Theory
MATLAB implementations of rough set-based data reduction algorithms with comprehensive algorithm explanations and practical applications
Rough Set Data Preprocessing: Discretization of Continuous Data
Rough set data preprocessing involves transforming raw data for subsequent analysis, primarily focusing on the discretization of continuous attributes to reduce computational complexity while maintaining data integrity.
Preliminary Data Preprocessing for Rough Set Theory with Discretization
Initial Rough Set Data Preprocessing with Focus on Continuous Data Discretization Techniques
Fundamental Algorithms of Rough Set Theory
Fundamental Algorithms of Rough Set Theory with Implementation Insights
Attribute Reduction Algorithm Based on Pawlak Attribute Significance
Attribute Reduction Algorithm Using Pawlak's Significance Measure with Implementation Insights