Description: sar is a Rough Set-based Attribute Reduction (aka Feature Selection) implementation. This is an implementation of ideas described, among other places, in the following paper:
Qiang Shen and Alexios Chouchoulas, A Modular Approach to Generating Fuzzy Rules with Reduced Attributes for the Monitoring of Complex Systems. Engineering Applications of Artificial Intelligence, 13(3):263-278, 2000.
rsar reads in a MIMO (Multiple Input, Multiple Output) dataset, performs RS-based feature selection on it, and returns the selected feature subset.
Four versions of the QuickReduct algorithm are supported, QuickReduct, QuickReduct III, QuickReduct IV and QuickReduct V (progressively faster implementations). QuickReduct II is a backward elimination version of QuickReduct and is not supported yet neither is exhaustive search for reducts. -sar is a Rough Set-based Attribute Reduction (aka Feature Selection) implementation. This is an implementation of ideas described, among other places, in the following paper:
Qiang Shen and Alexios Chouchoulas, A Modular Approach to Generating Fuzzy Rules with Reduced Attributes for the Monitoring of Complex Systems. Engineering Applications of Artificial Intelligence, 13(3):263-278, 2000.
rsar reads in a MIMO (Multiple Input, Multiple Output) dataset, performs RS-based feature selection on it, and returns the selected feature subset.
Four versions of the QuickReduct algorithm are supported, QuickReduct, QuickReduct III, QuickReduct IV and QuickReduct V (progressively faster implementations). QuickReduct II is a backward elimination version of QuickReduct and is not supported yet neither is exhaustive search for reducts. Platform: |
Size: 730112 |
Author:NH |
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Description: 粗糙集代码
data reduction with fuzzy rough sets or fuzzy mutual information
fuzzy preference rough set based feature evaluation and selection
-Rough code data reduction with fuzzy rough sets or fuzzy mutual information fuzzy preference rough set based feature evaluation and selection Platform: |
Size: 38912 |
Author:gq |
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Description: 引入能直接处理连续型数据的邻域粗糙集约简模型，给出一种基于邻域粗糙集模型和粒子群优化的特征选择算法。仿真实验结果表明该算法可以选择较少的特征，改善分类的能力。-employs the neighborhood rough set reduction model which can process the numerical features directly without discretization. Then the particle fitness function in particle swarm optimization (PSO) algorithm is built based on that model. Finally, a novel feature selection algorithm based on particle swarm optimization and neighborhood rough set reduction model is proposed. Experimental results prove that the new algorithm improves classification ability with fewer features selected. Platform: |
Size: 16384 |
Author:伍洁 |
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Description: 引入能直接处理连续型数据的邻域粗糙集约简模型，给出一种基于邻域粗糙集模型和粒子群优化的特征选择算法-Introduce neighborhood rough set reduction model can deal directly with continuous data, given the feature selection algorithm based on neighborhood rough set model and particle swarm optimization
Platform: |
Size: 13312 |
Author:伍洁 |
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Description: 离散人工蜂群算法用于解决特征选取问题，可以学习文章中将群智能算法离散化的方法，以便于解决离散问题。-Discrete artificial bee colony algorithm for solving feature selection problem, you can learn article will swarm intelligence algorithm discretization method, in order to solve the discrete problem. Platform: |
Size: 882688 |
Author:丁毅 |
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Search - rough set feature selection