Rough Set Theory Implementation
MATLAB implementation code for Rough Set Theory, featuring calculation of upper and lower approximation sets, core attributes, and reduction results with detailed algorithm explanations
Example of Attribute Reduction in Rough Set Theory with MATLAB Implementation
An example of attribute reduction in rough set theory implemented using MATLAB, featuring data mining and decision analysis applications with code implementation details
Minimum Reduction Algorithm Implementation Based on Rough Set Theory
This code implements the minimum reduction method using rough set theory, providing valuable reference for feature selection and data simplification in machine learning applications.
Rough Set Theory: A Framework for Uncertain Real-World Descriptions
Rough Set Theory provides a mathematical framework for handling uncertain descriptions of real-world data, with attribute reduction being one of its core components. This article elucidates the principles of Rough Set Theory and presents a heuristic-based knowledge reduction algorithm. The feasibility and effectiveness of the algorithm are demonstrated through MATLAB implementation examples, highlighting key functions and computational approaches.
Algorithm for Continuous Attribute Discretization Operations in Rough Set Theory
Implementation of continuous attribute discretization algorithms in rough set theory using MATLAB, including ready-to-use datasets for testing and analysis.
Attribute Reduction Example in Rough Set Theory with MATLAB Implementation
A practical example of attribute reduction using rough set theory, featuring MATLAB implementation approaches and algorithmic explanations for handling uncertain and incomplete data.
Continuous Attribute Discretization Algorithms in Rough Set Theory
Algorithms and Implementation Methods for Discretizing Continuous Attributes in Rough Set Theory