"classification feature" is a correct and usable phrase in written English.
It refers to a characteristic or attribute of something that helps to categorize or group it into a specific class or category. Example: The classification feature of this new species of bird is its distinct feather pattern, which sets it apart from other species in the same family.
The most important factor affecting the performance of a BCI (Brain Computer Interface) systems, is classification feature set.
The classification feature enables MPLS (Multi-protocol Label Switch) to support differentiated types of services (DiffServ) with needed QoS.
This kind of retrospective virtual screening is performed by using encoded molecule data and a neuro-fuzzy methodology for classification, feature selection, and rule generation.
We investigate the application of the IUBB algorithm in the design of imaging W-operators and in classification feature selection, using the average mean conditional entropy (MCE) as the cost function for the search.
An over 1000-element array of supervised classification results is generated by varying the underlying classification algorithm (Maximum Likelihood/Spectral Angle Mapper/Spectral Information Divergence), the remote sensing data stack (different multi-sensor data combination), and the set of hyperspectral channels used for classification (feature selection).
Data set would be processed based on classification feature.
By these processes, it can obtain nonlinear classification features efficiently.
Than obtained Wavelet coefficients by Discrete Wavelet Transform (DWT) and determined classification features from Wavelet coefficients.
It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features.
Because most good text classification features are positively correlated (i.e., they occur more often in than in ), one may want to explicitly rule out the selection of negative indicators.
We also use words and POSs as classification features.
