The overall purpose of this paper is to demonstrate how data preprocessing, training size
variation, and subsampling can dynamically change the performance metrics of imbalanced …

Many real applications have the imbalanced class distribution problem, where one of the
classes is represented by a very small number of cases compared to the other classes. One …

Scientific papers are a primary source of information for investigators to know the current
status in a topic or compare their results with other colleagues. However, mining biomedical …

Precision, Recall, and F1-score are metrics that are often used to evaluate model
performance. Precision and Recall are very important to consider when the data is …

Due to the recent escalation in the amount of text data available and used online, text
classification has become a staple for data analysts when extracting relevant information …

This article investigates the application of machine learning algorithms in the legal domain,
focusing on text classification tasks and addressing the challenges posed by imbalanced …

The extremely skewed data in artificial intelligence, machine learning, and data mining
cases are often given misleading results. It is caused because machine learning algorithms …

In supervised machine learning, with an imbalanced dataset, achieving better classification
in minority classes is a major challenge. In such situation, machine learning model shows …

The numbers of documents are increasing rapidly in a web format. Therefore, automatic
document classification is needed to help human to classify the documents. Text …

Imbalanced or unbalanced datasets are defined as the highly skewed distribution of target
variable in the field of machine learning. Imbalanced datasets have greatly caught the …