The random forest (RF) algorithm has several hyperparameters that have to be set by the
user, for example, the number of observations drawn randomly for each tree and whether …

Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

Objective. Computer vision-based up-to-date accurate damage classification and
localization are of decisive importance for infrastructure monitoring, safety, and the …

Objective. With climatic instability, various ecological disturbances, and human actions
threaten the existence of various endangered wildlife species. Therefore, an up-to-date …

Background To evaluate binary classifications and their confusion matrices, scientific
researchers can employ several statistical rates, accordingly to the goal of the experiment …

In pursuit of the perfect supervised NLP classifier, razor thin margins and low-resource test
sets can make modeling decisions difficult. Popular metrics such as Accuracy, Precision …

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 …

The evaluation of feature selection methods for text classification with small sample datasets
must consider classification performance, stability, and efficiency. It is, thus, a multiple …

Modern supervised machine learning algorithms involve hyperparameters that have to be
set before running them. Options for setting hyperparameters are default values from the …

Past research on facial expressions have used relatively limited datasets, which makes it
unclear whether current methods can be employed in real world. In this paper, we present a …