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.

2022年12月8日 · Recursive feature elimination (RFE) was used as the feature selection method for this analysis. RFE is a wrapper-type feature selection algorithm which identifies optimal combinations of features through generating a series of classification models and iteratively removing features that do not improve classification accuracy.

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.

To address these challenges, this paper proposes a novel module called remote feature exploration (RFE). This module can use remote elements to assist in the generation of local features, which, to a certain extent, provides the network with both local detail information extraction and a remote modeling capability.

The RF based on the feature selection result of RFE (RF-RFE) had the best performance in ten scenarios, and provided an overall accuracy of 90.73%, which was 0.97% higher than the RF without feature selection.

based on the Recursive Feature Elimination-Light Gradient Boosting Machine (RFE-LightGBM) algorithm is proposed in the classification stage of contaminants for recyclable containers. Based on the experimental results of using the proposed model, the difference in the classification accuracies on three da

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This study proposes a feature selection method that combines multi-temporal Sentinel-1 and Sentinel-2 data with SLPSO (social learning particle swarm optimization) and RFE (Recursive Feature Elimination), which reduces the impact of information redundancy and improves classification accuracy.

首先利用kd-tree方法为训练集选择有代表性的样本;其次利用SVM-RFE特征选择方法对数据预处理,提高分类器的性能;最后比较SVM-RFE特征选择前后及T-test特征选择后SVM的分类性能。

In the following, we first introduce the basic concept of the cross-bridge theory, and then introduce why rFE cannot be explained by the cross-bridge theory. Next, we introduce the two primary mechanisms that have been used to explain rFE: (i) the sarcomere length non-uniformity theory, and (ii) the titin theory.