2018年11月19日 · Using simulation studies based on time-to-event outcomes and three real datasets, we evaluate the three methods, based on pseudo-samples and kernel principal component analysis, and compare them with the original SVM …

This package contains an R implementation of the mSVM-RFE algorithm (Duan et al., 2005), including the option to cut the features by half each round (instead of one-by-one) if there are many features. The main function is adapted from http://www.uccor.edu.ar/paginas/seminarios/Software/SVM_RFE_R_implementation.pdf.

递归特征消除 （rfe） 是一种向后选择方法，它从所有特征开始，然后根据模型的性能递归删除最不重要的特征。 使用 交叉验证 技术评估模型的性能。

2023年11月19日 · 在进行实操之前，小果想为大家简单的介绍一下这两种算法的原理，SVM-RFE（support vector machine - recursive feature elimination）是基于 支持向量机 的机器学习方法， 通过删减svm产生的特征向量来寻找最佳变量；LASSO回归（logistic regression）也是机器学习的方法之一，通过寻找分类错误最小时的λ来确定变量，主要用于筛选特征变量，构建最佳分 …

2018年11月19日 · This paper extends the Recursive Feature Elimination (RFE) algorithm by proposing three approaches to rank variables based on non-linear SVM and SVM for survival analysis.

2017年12月26日 · Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications.

SVM-RFE （support vector machine-recursive feature elimination） 是基于 支持向量机 的机器学习方法，在 生物信息学 中，我们可以利用此方法对我们的 差异分析 后的差异 基因表达矩阵 进行基因的特征提取，根据自身设置分组变量的不同，最终达到通过SVM产生的特征向量来寻找最佳变量的目的，也就是利用机器学习的方法筛选特征基因，这些特征可以是正常和疾病分组，亦可 …

This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class ...

2018年11月19日 · This paper extends the Recursive Feature Elimination (RFE) algorithm by proposing three approaches to rank variables based on non-linear SVM and SVM for survival analysis. The proposed algorithms allows visualization of each one the RFE iterations, and hence, identification of the most relevant predictors of the response variable.

SVM-RFE. An iterative algorithm that works backward from an initial set of features. At each round it 1. fits a simple linear SVM, 2. ranks the features based on their weights in the SVM solution, and 3. eliminates the feature with the lowest weight.