搜索资源列表
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mRMR_0.9_compiled最小冗余和最大相关特征选取源代码，-This package is the mRMR (minimum-redundancy maximum-relevancy) feature selection method, whose better performance over the conventional top-ranking method has been demonstrated on a number of data sets in recent pu
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Feature Selection using Matlab.
The DEMO includes 5 feature selection algorithms:
• Sequential Forward Selection (SFS)
• Sequential Floating Forward Selection (SFFS)
• Sequential Backward Selection (SBS)
• Se
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Stepwise forward and backward selection of variables
using linear models
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利用最小互信息实现向量的特征选择，优化分类器的设计，原创-The use of mutual information to achieve the smallest feature selection vectors, optimizing the classifier design, originality
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协同模糊聚类建模通过特征选择和协同模糊聚类的模糊建模方法构建T-S模型，并用此模型对数据进行测试。-Collaborative fuzzy clustering modeling and collaboration through the feature selection fuzzy clustering TS fuzzy modeling method to build models and use this model of data for testing.
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基因算法实现的特征提取，实现平台是matlab-feature selection with genetic algorithm
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Neighborhood rough set based heterogeneous feature subset selection
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Feature Selection using matlab
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模式识别基本方法matlab源代码，包括最小二乘法、SVM、神经网络、1_k近邻法、剪辑法、特征选择和特征变换。-Basic method of pattern recognition matlab source code, including the least squares method, SVM, neural network, 1_k neighbor method, editing method, feature selection and feature transformatio
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Cluster based feature selection
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fisaher analysis feature selection
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feature selection based of Forwad search
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aco feature selection
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feature selection based on pearson coffeicients
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高校的ICA计算代码，广泛用于特征选择，降维，目标识别等-Colleges and universities ICA calculation code, widely used in feature selection, dimensionality reduction, target identification, etc.
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用粒子群优化算法进行特征选择和SVM参数优化-Using Particle Swarm Optimization algorithm of feature selection and SVM parameter optimization
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It's a Matlab toolbox designed by ASU. It is easy to use and you can use it to achieve the feature selection, classify and so on.
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蚁群算法用与特征选择，针对传统蚁群聚类算法收敛速度过慢的问题，提出一种对蚁群算法进行改进的聚类算法。而数据的高维使数据具有稀疏、不可聚集等特性，使聚类算法实验效果精度低和耗时大，将邻域特征选择与聚类算法结合，提出了一种蚁群聚类优化的邻域特征选择算法(Ant colony algorithm and feature selection)
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最小冗余最大相关性（MRMR）（MRMR.M）
需要外部库。详情请见MRMR。下载一个更新版本的互信息工具箱
偏最小二乘（PLS）回归系数（ReGCOEF.m）
使用MATLAB统计工具箱中的PLSReress
ReliefF（分类）和RReliefF（回归）（ReleFracePr.M.）
从Matlab STATS工具箱中包装Releff.m。这是Matlab R2010B以后提供的。
ReliefF的另一个选择是使用ASU特征选择工具箱中的代码。这使用WEKA
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用二进制遗传算法做特征选择，此算法效率高，选择的特征数目少。(The binary genetic algorithm is used for feature selection, which has high efficiency and few features.)
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