One of the major factor which can affect the accuracy of text classification is the imbalanced
dataset. In order to find the suitable method to handle this issue, six different ensemble …

The problem of imbalanced data can be frequently found in the real-world data. It leads to
the bias of classification models, that is, the models predict most samples as major classes …

Unbalanced datasets make it hard for text classifiers to learn well. Having limited information
in minority classes makes it difficult to classify the unbalanced texts. In this study, a BERT …

In this paper, we propose a new random forest (RF) based ensemble method, F ores T exter,
to solve the imbalanced text categorization problems. RF has shown great success in many …

Textual data suffers from two main problems, large number of features and class imbalance.
Many conventional approaches and their variants exist in literature to solve both these …

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 …

The problem of imbalanced data has a heavy impact on the performance of learning models.
In the case of an imbalanced text dataset, minority class data are often classified to the …

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 …

With the increasing amount of textual data generated online, an automatic system for text
classification is imperative. However, classification models face the challenge of limited and …

Imbalanced datasets are datasets with one or more underrepresented classes when
compared to other classes. Such datasets pose problems during classification due to the …