A binary classification problem is common in medical field, and we often use sensitivity,
specificity, accuracy, negative and positive predictive values as measures of performance of …

Receiver operating characteristic (ROC) curves are very powerful tools for measuring
classifiers' accuracy in binary-class problems. However, their usefulness in real-world multi …

The interest in statistical classification for critical applications such as diagnoses of patient
samples based on supervised learning is rapidly growing. To gain acceptance in …

Many expert systems solve classification problems. While comparing the accuracy of such
classifiers, the cost of error must frequently be taken into account. In such cost-sensitive …

Deep learning neural network models such as multilayer perceptron (MLP) and
convolutional neural network (CNN) are novel and attractive artificial intelligence computing …

Probability estimation for binary and multicategory outcome using logistic and multinomial
logistic regression has a long‐standing tradition in biostatistics. However, biases may occur …

It is popular to evaluate the performance of classification algorithms by k-fold cross
validation. A reliable accuracy estimate will have a relatively small variance, and several …

The F-measure, which has originally been introduced in information retrieval, is nowadays
routinely used as a performance metric for problems such as binary classification, multi-label …

In binary classification problems, the area under the ROC curve (AUC) is commonly used to
evaluate the performance of a prediction model. Often, it is combined with cross-validation in …

Binary classification is a common task for which machine learning and computational
statistics are used, and the area under the receiver operating characteristic curve (ROC …