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[matlab] Relief

Description:
Platform: | Size: 709632 | Author: wangli | Hits:

[matlab] basic_PSO_with_w_c

Description: 带有收缩因子和惯性权重的基本PSO粒子群算法源代码。本源代码模块化编写，结构清晰，便于改进和做数值实验-With contraction factor and inertia weight PSO basic particle swarm algorithm source code. Source code modular preparation, structure, clear, easy to improve and to do numerical experiments
Platform: | Size: 3072 | Author: 楚湘华 | Hits:

[Windows Develop] ReliefTest

Description:
Platform: | Size: 9302016 | Author: 袁泉 | Hits:

[Other] reliefF

Description: reliefF algorithm for gene selection
Platform: | Size: 1024 | Author: Yi Zhang | Hits:

[Windows Develop] source

Description:
Platform: | Size: 685056 | Author: jiaojiao | Hits:

[AI-NN-PR] FeatureSelection

Description: Feature Selection using Matlab. The DEMO includes 5 feature selection algorithms: • Sequential Forward Selection (SFS) • Sequential Floating Forward Selection (SFFS) • Sequential Backward Selection (SBS) • Sequential Floating Backward Selection (SFBS) • ReliefF Two CCR estimation methods: • Cross-validation • Resubstitution After selecting the best feature subset, the classifier obtained can be used for classifying any pattern. Figure: Upper panel is the pattern x feature matrix Lower panel left are the features selected Lower panel right is the CCR curve during feature selection steps Right panel is the classification results of some patterns. This software was developed using Matlab 7.5 and Windows XP. Copyright: D. Ververidis and C.Kotropoulos AIIA Lab, Thessaloniki, Greece, jimver@aiia.csd.auth.gr costas@aiia.csd.auth.gr-Feature Selection using Matlab. The DEMO includes 5 feature selection algorithms: • Sequential Forward Selection (SFS) • Sequential Floating Forward Selection (SFFS) • Sequential Backward Selection (SBS) • Sequential Floating Backward Selection (SFBS) • ReliefF Two CCR estimation methods: • Cross-validation • Resubstitution After selecting the best feature subset, the classifier obtained can be used for classifying any pattern. Figure: Upper panel is the pattern x feature matrix Lower panel left are the features selected Lower panel right is the CCR curve during feature selection steps Right panel is the classification results of some patterns. This software was developed using Matlab 7.5 and Windows XP. Copyright: D. Ververidis and C.Kotropoulos AIIA Lab, Thessaloniki, Greece, jimver@aiia.csd.auth.gr costas@aiia.csd.auth.gr
Platform: | Size: 3283968 | Author: driftinwind | Hits:

[AI-NN-PR] feature_selection

Description: MRMR和relieff特征选择方法，很经典的，简单易用！-The the MRMR and the relieff feature selection method, very classic, simple and easy to use!
Platform: | Size: 103424 | Author: 代江艳 | Hits:

[matlab] reliefF

Description: reliefF源码 用法及说明 简单易懂的-reliefF source usage, and easy to understand instructions
Platform: | Size: 11264 | Author: 郑宗琳 | Hits:

[Console] thesis

Description: relieff算法一种加权值，选择重要属性-relieff algorithm
Platform: | Size: 1974272 | Author: meizhigang | Hits:

[Algorithm] code-of-reliefF-algrithm

Description: 给出了reliefF算法的matlab源代码，该算法用于处理目标属性为连续值的回归问题。是由relief算法拓展所得，可以处理多类别问题。-ReliefF algorithm matlab source code is given, and the algorithm is used for processing target attribute is continuous values of regression problems.By expanding income relief algorithm, can deal with many categories.
Platform: | Size: 8192 | Author: 李斌 | Hits:

[Special Effects] code-Feature-Selection-using-Matlab

Description: 主要完成图像特征出提取，包括5个特征选择算法：SFS，SBS，SFBS-Description The DEMO includes 5 feature selection algorithms: Sequential Forward Selection (SFS) Sequential Floating Forward Selection (SFFS) Sequential Backward Selection (SBS) Sequential Floating Backward Selection (SFBS) ReliefF
Platform: | Size: 3284992 | Author: fuhuan | Hits:

[Other] fsReliefF

Description: 著名的ReliefF特征过滤算法源代码，可用于模式识别-The famous ReliefF feature filtering algorithm source code, can be used for pattern recognition
Platform: | Size: 6144 | Author: 大熊猫 | Hits:

[GIS program] featureselection

Description: 使用MATLAB，ReliefF算法应用于特征提取，适用于shp属性数据。-Using MATLAB, the ReliefF algorithm is applied to feature extraction and is suitable for SHP attribute data.
Platform: | Size: 2509824 | Author: danial | Hits:

[Shell api] ReliefF-2

Description: 由于Relief算法比较简单，运行效率高，并且结果也比较令人满意，因此得到广泛应用，但是其局限性在于只能处理两类别数据，因此1994年Kononeill对其进行了扩展，得到了ReliefF作算法，可以处理多类别问题。该算法用于处理目标属性为连续值的回归问题。-Because Relief algorithm is relatively simple, high efficiency, and the results are more satisfactory, so widely used, but its limitation is that only two types of data can be processed, so Kononeill in 1994 to expand it, get ReliefF algorithm , Can handle multiple categories of problems. The algorithm is used to deal with the regression problem that the target attribute is a continuous value.
Platform: | Size: 2048 | Author: 张国顺 | Hits:

[matlab] fs_sup_relieff

Description: Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重： 如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules: If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)
Platform: | Size: 4096 | Author: voriarty | Hits:

[matlab] feature-selection-master

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 | Hits:

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