2022年12月8日 · To investigate this, a series of radial basis function (RBF) support vector machines (SVM) supervised machine learning land cover classifications of Sentinel-2A Multispectral Instrument (MSI) imagery were conducted to assess the transferability of recursive feature elimination (RFE)-derived feature sets between different classification models ...

2020年4月20日 · Recursive feature elimination (RFE) is a feature selection method that attempts to select the optimal feature subset based on the learned model and classification accuracy. Traditional RFE sequentially removes the worst feature that causes a drop in “classification accuracy” after building a classification model.

2024年11月5日 · SVM-RFE (Recursive Feature Elimination based on Support Vector Machine) is a feature selection method based on SVM (Guyon et al. 2002). It optimizes feature subsets by recursively removing features that contribute least to classification and is suitable for several types of datasets, including linearly separable and nonlinearly separable data.

Although convolutional neural networks (CNNs) have produced great achievements in various fields, many scholars are still exploring better network models, since CNNs have an inherent limitation—that is, the remote modeling ability of convolutional kernels is limited. On the contrary, the transformer has been applied by many scholars to the field of vision, and although it has a strong global ...

2024年7月1日 · Recursive feature elimination (RFE), is commonly used for feature selection. A ranking of features as well as candidate subsets, with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset.

2024年7月4日 · rfe 是一种有效的特征选择方法，适用于高维数据集中的特征筛选任务。通过递归地消除不重要的特征，rfe可以提高模型的性能，减少过拟合，并提升计算效率。

2018年6月15日 · We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced.

2020年5月4日 · Our results show that RF-RFE outperforms SVM-RFE and KWS on the task of finding small subsets of features with high discrimination levels on PTR-MS data sets.

vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines

2023年7月7日 · Therefore, in order to obtain a perfect medical segmentation prediction graph, the network should not only have a strong learning ability for local details, but also have a certain distance modeling ability. To solve these problems, a remote feature exploration (RFE) module is proposed in this paper.