The phrase "accuracy of features" is correct and usable in written English.
It can be used when discussing the precision or correctness of specific characteristics or attributes, often in contexts like technology, data analysis, or product descriptions.
Example: "The accuracy of features in this software is crucial for ensuring reliable results."
Alternatives: "precision of attributes" or "correctness of characteristics".
Experimental results of a classification task based on microarray and image databases validate the effectiveness and accuracy of features obtained by our feature selection method.
In such methods, the accuracy of initial selection of subsets using exhaustive forward or backward search of the features [42] would significantly impact the accuracy of features obtained with a given feature selection criterion.
Weighting by frequency spread has benefit of reducing those ill-conditioned responses that have the low frequency spread, as well as improving the localization accuracy of features, especially the smoothed features whose responses are normally uniform [20].
In the experiments, we use the DAGSVM with Raw kernel as the classifier for testing the predictive accuracy of features selected by different methods.
In Table 2, the predictive accuracy of features selected by three methods, MIFS, MRMR and SVM-RFE, over twelve datasets are compared.
Finally, we compared the classification accuracy of features selected by the GA many times over 200 runs in each fold (shown in Fig. 2, for a large number of feature (gene) set sizes) with the accuracy of features selected in single runs.
If the image of the grid is smaller, the accuracy of feature detection will diminish and the detector will begin to fail.
This paper describes a localisation framework that combines the accuracy of feature maps with the scalability of topological maps.
While promising, most existing works apply convex methods, which may be suboptimal in terms of the accuracy of feature selection and parameter estimation.
Ensemble learning has been used to improve the accuracy of feature gene selection [ 26] and cancer classification [ 27– 29].
The noise component of speckle results in an overall grainy appearance of images acquired using OCT, which can have an significant impact on the accuracy of feature detection and analysis using such data.
