Sentence examples for binary classifiers and the from inspiring English sources

Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design.

The models here are binary classifiers and the following measures have been utilised to assess the predictive performance of the models based upon true positive (TP), false positive (FP), true negative (TN) and false negative (FN) result classification.

For the training of binary classifiers and for the selection of predictive features, we obtained OGs and TSGs from the Cancer Gene Census (CGC) [ 47] as well as NGs, as described [ 19].

Similar to the OAO method, the DAG method also needs to construct k k − 1)/2 binary classifiers and obtain the corresponding decision functions of these classifiers.

This last step was accomplished by multiplying the vector representing the classifiers output with the ECOC decoding matrix M of KxL with entries mi,j ∈ {−1, 0, 1} where L is the number of binary classifiers and K is the number of classes (i.e., 8 target directions).

This sparse, class-invariant transformation helps reduce bias of weak binary classifiers and improve overall performance.

SVMs are binary classifiers and multi-class classification is realized by developing an SVM for each pair of classes.

The modeling tool is a binary classifier, and thus the model predicts whether the chemical is toxic or not.

SVM is a binary classifier, and thus to obtain the ROC curves, we calculated the distance from the decision plane and used it as the prediction score (Ishida and Kinoshita, 2007).

Each internal node corresponds to a binary classifier, and each leaf node corresponds to a class.

SVM is a binary classifier, and its decision function can be expressed as.