Description: 经典的LDA特征选择算法，用matlab实现，包括数据集-LDA classic feature selection algorithm, using matlab to achieve, including a data set Platform: |
Size: 13312 |
Author:shall |
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Description: 这个程序实现了Francis R. Bach的Bolasso算法，用于特征选取和预测。主要用于高纬度问题的特征选取，它使用了带有Bootstrap方法的自助抽样的正则化回归，并使用了Karl Skoglund的lars实现。-This procedure achieved Francis R. Bach s Bolasso algorithms for feature selection and forecasting. The main problem for high-latitude feature selection, it uses a method of self-help Bootstrap sampling Tikhonov reunification, and Karl Skoglund used to achieve the lars. Platform: |
Size: 198656 |
Author:xuechaoling |
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Description: feature selection from iris data set it will use statistical methods and get the best set of features then use graphs to classify the data-feature selection from iris data set it will use statistical methods and get the best set of features then use graphs to classify the data Platform: |
Size: 28672 |
Author:madurangak |
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Description: 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 publications. This version uses mutual information as a proxy for computing relevance and redundancy among variables (features). Other variations such as using correlation or F-test or distances can be easily implemented within this framework, too.
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Size: 1020928 |
Author:韩华 |
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Description: This a code for feature selection. Which combines minimum redundency and max relevance and Ftest. Originally it is written for gene selection but can be used for any kind of feature selection.-This is a code for feature selection. Which combines minimum redundency and max relevance and Ftest. Originally it is written for gene selection but can be used for any kind of feature selection. Platform: |
Size: 2048 |
Author:vida |
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Description: 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. Platform: |
Size: 8908800 |
Author:liang911
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Description: 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工具箱的ReleFEF，因此需要额外的库。请参阅相应的文档。
费雪评分（Fisher评分）
围绕ASFS特征选择工具箱围绕FSFisher。M(Minimum Redundancy Maximum Relevance (mRMR) (mRMR.m)
Needs external library. See mRMR.m for details.
Download a newer version of the mutual information toolbox
Partial Least Squares (PLS) regression coefficients (regCoef.m)
Uses plsregress.m from MATLAB statistics toolbox
ReliefF (classification) and RReliefF (regression) (relieffWrapper.m)
Wraps around relieff.m from the MATLAB stats toolbox. This is available MATLAB r2010b onwards.
Another option for ReliefF is to use the code from ASU Feature Selection toolbox. This uses ReliefF from weka toolbox and hence needs additional libraries. Please see the corresponding documentation.
Fisher Score (fisherScore.m)
Wraps around fsFisher.m from the ASU Feature Selection toolbox) Platform: |
Size: 11264 |
Author:smilingcost |
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