Although few performance evaluation instruments have been used conventionally in
different machine learning-based classification problem domains, there are numerous ones …

Selecting the proper performance metric constitutes a key issue for most classification
problems in the field of machine learning. Although the specialized literature has addressed …

This paper proposes a systematic benchmarking method called BenchMetrics to analyze
and compare the robustness of binary classification performance metrics based on the …

Several performance metrics are currently available to evaluate the performance of Machine
Learning (ML) models in classification problems. ML models are usually assessed using a …

Binary classification is one of the most frequent studies in applied machine learning
problems in various domains, from medicine to biology to meteorology to malware analysis …

The correct selection of performance metrics is one of the most key issues in evaluating
classifier's performance. Although many performance metrics have been proposed and used …

A major issue in the classification of class imbalanced datasets involves the determination of
the most suitable performance metrics to be used. In previous work using several examples …

Predictive performance evaluation is a fundamental issue in design, development, and
deployment of classification systems. As predictive performance evaluation is a …

A variety of different performance metrics are commonly used in the machine learning
literature for the evaluation of classification systems. Some of the most common ones for …

In recent years, researchers have made a great success on the automatic classification and
detection of malware utilizing machine learning methods. However, most machine learning …