Sentence examples for four categories of features from inspiring English sources

Through our study, we propose four categories of features that reflect designers' concerns in judging review helpfulness.

Furthermore, for the cases where review ratings are not steadily available, we have proposed to use regression to predict and interpret review helpfulness with the help of the aforementioned four categories of features that are entirely extracted from review content.

For gene-specific classifiers, the highest accuracy (83.2%) was achieved by combining all four categories of features (see Methods).

The four categories of features are composition information, structural and functional information, physicochemical properties and evolutionary information and the three kinds of coding methods are overall composition-transition-distribution called OCTD Globall method), autocross-covariance (ACC) transformation (Nonlocal method) and split amino acid (SAA) Transformation (Local method).

Our approach is based on a clear separation between the two categories of features, and uses algebra, respectively coalgebra to formalise them.

There are three categories of features: tweet influence: the importance of a tweet within the conversation where it appears is estimated using social influence.

The functional analysis implemented in our web server for SNPs in trait-associated pathways considers linkage disequilibrium (LD) information, three categories of features (coding, non-coding and eQTLs) and enrichment analysis, which is a very comprehensive tool/module in comparison to available web-based tools for functional analysis (Table S6).

The distribution of the selected properties between these two categories of features is shown in Table 1, Columns 8 and 9. Amino acid composition properties are capable of discerning highly differential AMP family from other families, such as in the case of type A lantibiotic.

Since most of those algorithms consist of both feature extraction and classification, we provide a survey on three categories of feature extraction methods, including the voxel-, vertex- and ROI-based ones, and four categories of classifiers, including the linear discriminant analysis, Bayes classifiers, support vector machines, and artificial neural networks.

Three categories of feature selection methods can be used: filters, wrappers, and embedded methods [ 242, 243].

Within that framework, we define four categories of tie features: intensity, temporal, structural and intimacy features, and we will try to characterize which ties are the strongest (more persistent) according to these variables.