Sentence examples for used other classifications from inspiring English sources

The remaining 10.3 % used other classifications, such as Ministry of Health (MoH) adaptations of either the WHO 1997 or WHO 2009 guidelines, or the authors' own classifications constructed on the basis of clinical, laboratory or pathological findings.

This resource not only uses gene ontology categories in the standard analysis, but also uses other classifications (e.g. "SP_PIR_KEYWORDS"); details are available online (http://david.abcc.ncifcrf.gov/) and from [ 26].

Further development of the proposed approach includes perfecting the local and global feature selections, as well as testing using other classification techniques.

Quantile classification most closely represents the input data trends that are poorly represented using other classification methods, such as Jenks-Natural breaks, equal interval, standard deviation, and geometric classifications.

In principle, one could use other classification models, for example linear discriminant analysis (LDA), a standard classification algorithm that combines a model such as (7) with a marginal model for (x_{t}.) However, Hastie et al. (2009) have argued that it is often preferable to stick to a model such as (7) rather than rely on LDA in practice.

Results in Additional file 1 illustrate that the IFR procedure can be effective even when using other classification engines.

21 Furthermore, predictors that might otherwise be masked by their correlation with other variables, using other classification methods, can contribute to the Random Forest classifier.

This is more evident when we compare with the decrease in accuracy observed in the LAD experiments with the experiments using other classification algorithms, shown in Table 4.

The RSSI with relevant plaques in our study would be regarded as small artery diseases using other classification methods such as TOAST (Trial of ORG 10172 in Acute Stroke Treatment) [ 22] and SSS-TOAST (Stop Stroke Study-TOAST) [ 23] using conventional imaging techniques.

However, it should be noted that, although some of these complex anomalies may be classified equally successfully by using other existing classifications (e.g. U2bC2 of the ESHRE/ESGE classification equates to C1U1c of the VCUAM classification), those systems have other disadvantages (Grimbizis and Campo, 2010), which would limit their use.

Comparable results were also reported when the authors used other supervised learning algorithms for classification, such as support vector machine (SVM) and k-nearest neighbours (k-NN).