Jun 19, 2020 · Building a Multi-Class text classification model by considering various independent categorical features which are important along with the text description feature when dataset is...

Jan 1, 2019 · When tackling imbalanced text data classification, decisions must be made at several distinct stages: How to rep- resent the text information? What is the classifier algorithm …

Oct 16, 2022 · You need to balance the distribution for your classifier not for a reader of text data. In principle we can use SMOTE as traditional with some Dimensionality Reduction step. 1) …

Jan 2, 2024 · Addressing imbalanced data in classification is crucial for fair model performance. Techniques include resampling (oversampling or undersampling), synthetic data generation, …

Aug 2, 2020 · Instead of naïvely or implicitly applying a default threshold of 0.5, or immediately re-training using re-balanced training data, we can try using the original model (trained on the …

Dec 6, 2022 · Most algorithms are designed to work best when the data is balanced, meaning that all classes are represented roughly equally. If there is a class imbalance, the algorithm may …

Mar 15, 2025 · This study proposes Refined Classification using Overlap data with Bagging and Genetic Algorithms (ReCO-BGA), a novel refinement-based hybrid framework for textual data …

Apr 16, 2023 · Imbalanced text data is a common challenge in machine learning, particularly in natural language processing (NLP) tasks. It occurs when the distribution of classes in the …

In general, if you're looking to account for a class imbalance in your training data it means you have to change to a better suited loss function. Specifically for class imbalance, you want to …

Mar 14, 2025 · Handling imbalanced data in text classification is a complex but rewarding challenge. As we've explored, there are numerous techniques and strategies available, from …