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粗糙集matlab算法，有属性约简，值约简，规则生成等多种算法。-Neighborhood rough set based feature evaluation and reduction
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Neighborhood rough set based feature evaluation and reduction
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Neighborhood rough set based feature evaluation and reduction
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Neighborhood rough set based feature evaluation and reduction
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Neighborhood rough set based heterogeneous feature subset selection
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新基于邻域粗糙集的前向贪心的属性约简算法-New neighborhood-based forward greedy rough set attribute reduction algorithm
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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 particl
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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
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利用邻域粗糙集的属性约简：
从数值数据计算约简，属性数据和它们的混合物与邻域粗糙集。
两种邻域的使用：清晰和模糊。-Use of neighborhood rough set attribute reduction: reduction from numerical data, attribute data and their mixtures with neighborhood rough set. The use of two kinds of neighborhoods: clea
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依据Neighborhood classifier编写的.m程序-Metric space Neighborhood Rough set Reduction Classifier Norm
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这是天津大学胡清华老师在粗糙集邻域领域做的最经典的源码，同学们可以在此基础上学习和修改，入口程序已经写好，需要其他方法可以自己添加，MAIN.m是入口程序，参数的意思在子函数里讲的很明白，调用了featureselect_FW_fast.m用来属性约简，几个clsf_dpd文件是使用不同的距离公式来计算属性重要度，选择得到属性结果，使用crossvalidate.m十折交叉算法来计算计算算法精度，该段代码调用了几个分类器，C4_5.m是决策树，KNN.m是最近邻分类器，NEC.m是类似于KNN的
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利用基于排序的邻域粗糙集算法对具有高维属性的元数据进行属性约简，删减多余的无关属性，避免模型的过拟合，提高模型预测精度和模型运算速度-The algorithm based on the ordered neighborhood rough set is used to reduce the attributes of the high dimension attributes, to eliminate the redundant irrelevant attributes, to avoid
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天津大学胡清华老师的邻域粗糙集经典主程序代码-Tianjin University neighborhood Hu Qinghua teacher of classical rough set main code,
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胡清华邻域粗糙集源码，matlab源代码，方便可用(Hu Qinghua neighborhood rough sets source code, matlab source code, easy to use)
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