2 天前 - … images. In [22], the real-time application based on PSO and one-dimensional CNN
and SVM … in order to verify the power of the feature selection method used in this study. Also, …

22 天前 - … After dimensionality reduction and screening, eight … ), support vector machine (SVM),
K-nearest neighbors (KNN), … The above studies used various methods for feature selection …

29 天前 - … of the SVM algorithm from 81% to 83% and KNN from 68% to … of PCA should be
considered based on the characteristics of … dimensionality reduction methods for medical image …

40 天前 - … : Support Vector Machine (SVM), K-nearest neighbor (KNN… was used for the first
dimensionality reduction, and the L1 … dimensionality reduction to select the final features for …

196 天前 - … task, dimensionality reduction is essential for optimal performance. Feature selection
… In contrast, the SVM classifier has a low generalization error rate, good learning ability, …

260 天前 - … KNN and SVM are used in the classification task. By comparing the performance of
different dimension reduction techniques and feature selection … of mammographic images. …

267 天前 - … In the study, high success was achieved in cancer detection with the suggested
adaptive PSO algorithm for feature selection from the microarray dataset and SVM, k-NN, and …

293 天前 - … different classifiers (ie, RF, SVM and KNN); 2) determine the … and 3) explore feature
selection and dimensionality reduction … optimal input bands and features for the classifiers. …

306 天前 - … Finally, a Stacking ensemble learning model based on algorithms such as KNN,
RF, SVM, … that employing the feature selection and feature dimensionality reduction methods …

337 天前 - … effect of SVM is better than that of RF and KNN. Additionally, when the image spatial
… the Boruta-Shap feature selection method can reduce data dimensionality and improve …