Sentence examples for building a classifier and from inspiring English sources
Development of a biomarker classifier involves two distinct components: (1) a procedure for building a classifier and (2) validation/evaluation procedure for estimating the error rate of biomarker.
The third layer (phishGILLNET3) further expands phishGILLNET2 by building a classifier from labeled and unlabeled examples by employing Co-Training.
Thus, 10-PS were considered the limit for building a classifier of acceptably high and robust predictivity (Fig. 7a).
After learning the BN structure, we topologically sort the nodes of the graph and then, start building a classifier for each node in this order (see Fig. 4).
al. use, in contrast to the one presented here, does not attempt to computationally and algorithmically highlight differences in phenotypes by building a classifier around measurable network features.
Several machine-learning algorithms were investigated in building a classifier to predict novel cancer genes, including naïve Bayes, logistic regression and support vector machines (SVM), all of which have been widely used for pattern classification and regression problems.
One-class uses only the information for the target class (positive class) building a classifier which is able to recognize the examples belonging to its target and rejecting others as outliers.
According to standard procedure, building a classifier using machine learning is a fully automated process that follows the preparation of training data by a domain expert.
Our aim was to identify the best characteristics that discriminate BD families from control by building a classifier with the main characteristics found from different scales.
Previous works address this class-description problem by either analyzing the correlation of each attribute with the class, or by producing rules as in building a classifier.
In conclusion, we have demonstrated that a supervised machine learning approach to building a classifier for finding patients that might have FH using EHR data is feasible with a positive predictive value of 0.88, sensitivity of 0.75 and specificity of 0.99.
