Sentence examples for the majority of classification from inspiring English sources

In this work, the majority voting (MV) is the majority of classification type among all four sensor positions.

In this work, the majority voting is the majority of classification types among all four sensor positions.

The majority of classification errors occurred at Level I of the classification hierarchy (i.e., misclassifications were generally to a different Level I class, not to a Level II class within the same Level I class).

While some algorithms, particularly Naive Bayes and SVM, showed often large improvements versus the non-discretized data, the majority of classification algorithms (K-nn, AdaBoost, C4.5, Random Forest and Logistic Regression) typically did not show any improvement across the discretization algorithms in these datasets.

The majority of classification errors were mainly observed in the periventricular white matter regions where signal intensities approached that of CSF, resulting in WM being misclassified as CSF (Figure 2).

The teams agreed that the majority of classification systems failed to provide sufficient instructions on use.

The majority of classifications were metabolic pathways and biosynthesis of secondary metablolites, which represented 19.15% and 6.05% of total annotated unigenes, respectively.

For the majority of the classification and analytical methods evaluated, estimates of effect were biased toward the null.

The majority of the classification systems was based on a judgemental approach, relying on clinical experience and intuition, and a minority was based on a statistical approach and prospective study designs.

The majority of the classification analyses produced an average testing accuracy, calculated over 10 cross-validation datasets, greater than or close to 90%, with the exception of two SNP-only analyses; the 500 K-only and Array6.0-only analyses had relatively low testing accuracies for the classification of samples from two closely related ethnic populations, CHB and JPT.

The majority of protein classification methods rely on alignment to known protein sequences to identify the similarities and to build various forms of models (e.g., regular expression patterns [ 4], protein fingerprints [ 5], position-specific scoring matrices [ 6], and profile hidden Markov models [ 7]).