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 …

Imbalanced data will affect the accuracy of text classification, in order to solve this issue, 11
different algorithms are used to resampling the dataset. Results show that, 5 different …

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 …

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 …

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 …

It is well known that classifiers trained using imbalanced datasets usually have a bias toward
the majority class. In this context, classification models can present a high classification …

This study focuses on analyzing effective strategies to handle imbalanced data in text
classification. The study employs probabilistic Naive Bayes as a common machine learning …

In multiclass text classification, the performance of classifier is usually very low while the
data distribution is very uneven. The reason may be that the majority class has a great …

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 …

Some of the most widely used text classification methods, such as the K-Nearest Neighbor
(KNN) algorithm, the Native Bayes (NB) algorithm and the Support Vector Machine (SVM) …