Considering the unique temporal and spatial distribution characteristics, the DITI-based model combined with the fuzzy cluster analysis method provided more accurate classification of the rainfall distribution zones.
Finally, the high density of detectors allowed by the small size of the antennas permits a finer scattered-field sampling angular resolution, resulting in a more accurate classification system.
The ensemble framework is applied to sentiment classification tasks, with the aim of efficiently integrating different feature sets and classification algorithms to synthesize a more accurate classification procedure.
Vegetation was extracted by using a Normalized Difference Vegetation Index (NDVI) image at first, and then a more accurate classification result of shrub and grassland is obtained by integrating the height information from LIDAR data.
We need to ignore such data for more accurate classification.
Finally, Asm command sequences also produce a more accurate classification result as well which is shown in Section 7.3.
Thus, to emphasize the importance of sensitive features, a feature-weighting step is needed to guarantee a more accurate classification result.
Feature selection methods have been applied in classification problems to select a reduced feature subset from the original set to achieve a faster and more accurate classification.
A features space with a smaller dimension will allow more accurate classification (regardless the classifier) due to data organization and projecting data to another space in which the discrimination is more obvious.
Since this work concerns primarily the feasibility of traffic monitoring only based on RF receiving, so a feature selection step for more accurate classification is not a part of this work.
SSL is an ML approach that typically uses a large amount of unlabeled and a small amount of labeled data to build a more accurate classification model than would be built using only labeled data.
