We performed a systematic evaluation of twelve feature selection methods for preserving cellular trajectories in noisy single-cell data. Methods were grouped into five general categories prior to evaluation: supervised, similarity, subspace-learning, variance, and baseline approaches. For more details on ...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Then seven algorithms Ba...
In summary, we identified a 38 gene signatures with ideal performance when classifying COAD tumor from normal samples by using feature selection methods in this study. The majority of the 38 DEGs were significantly up-regulated in tumor samples compared to normal samples. In the Bayesian network,...
Product configuration is a feature selection process, taking a feature model fm as input and producing a feature configuration fc that is permitted by fm as output according to variability constraints. This can be reduced to a multistep configuration problem, i.e., the process of producing a se...
Surprisingly, the regions where ideal HCT succeeds and fails make exactly the same partition of the phase diagram. Other threshold methods, such as false (feature) discovery rate (FDR) threshold selection, are successful in a substantially smaller region of the phase space than either HCT or ...
Surprisingly, the regions where ideal HCT succeeds and fails make exactly the same partition of the phase diagram. Other threshold methods, such as false (feature) discovery rate (FDR) threshold selection, are successful in a substantially smaller region of the phase space than either HCT or ...
The venn diagram demonstrates that fold change method is more consistent with current feature selection methods when the sample number is not so large. Figure 6 Venn diagram for the results obtained by different methods. a) Comparing t-test, SMLR, SVM-RFE, and LPFS methods by checking the ...
Returns or sets a value (constant) that specifies how Microsoft Excel handles calls to methods and properties that require features that aren’t yet installed. Read/write MsoFeatureInstall. C# 複製 public Microsoft.Office.Core.MsoFeatureInstall FeatureInstall { get; set; } Property Value Mso...
As one of the supervised feature selection methods, the Fisher score algorithm selects each feature independently in accordance with their scores. Here, Feature selection using the Fisher score algorithm results in a list of genes that are ranked by their importance. A previous study showed that ...
The author uses the feature vector of the similarity matrix to generate a new subspace, that is, the similarity subspace, and finally uses the feature selection method to generate a new complete similarity subspace, so as to realize the feature extraction of the data. In the process of data ...