# A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data

arXiv: https://arxiv.org/abs/2411.01013 · HTML: https://mlsanity.com/paper/2411.01013/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 94 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 11 of 11)

- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Weakly-Supervised Neural Text Classification](https://mlsanity.com/paper/1809.01478/) (2018)
- [Text Classification Using Label Names Only: A Language Model Self-Training Approach](https://mlsanity.com/paper/2010.07245/) (2020)
- [The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures](https://mlsanity.com/paper/2104.10640/) (2021)
- [Semi-Supervised Text Classification via Self-Pretraining](https://mlsanity.com/paper/2109.15300/) (2021)
- [Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets](https://mlsanity.com/paper/1802.05033/) (2018)
- [Imbalanced classification: a paradigm-based review](https://mlsanity.com/paper/2002.04592/) (2020)
- [Lacking the embedding of a word? Look it up into a traditional dictionary](https://mlsanity.com/paper/2109.11763/) (2021)
- [Evolving Multi-label Classification Rules by Exploiting High-order Label Correlation](https://mlsanity.com/paper/2007.11609/) (2020)
- [Sequential Targeting: an incremental learning approach for data imbalance in text classification](https://mlsanity.com/paper/2011.10216/) (2020)
- [Enhancing Next Destination Prediction: A Novel Long Short-Term Memory Neural Network Approach Using Real-World Airline Data](https://mlsanity.com/paper/2401.12830/) (2024)

## Abstract (arXiv)

In real-world applications, as data availability increases, obtaining labeled data for machine learning (ML) projects remains challenging due to the high costs and intensive efforts required for data annotation. Many ML projects, particularly those focused on multi-label classification, also grapple with data imbalance issues, where certain classes may lack sufficient data to train effective classifiers. This study introduces and examines a novel oversampling method for multi-label text classification, designed to address performance challenges associated with data imbalance. The proposed method identifies potential new samples from unlabeled data by leveraging similarity measures between instances. By iteratively searching the unlabeled dataset, the method locates instances similar to those in underrepresented classes and evaluates their contribution to classifier performance enhancement. Instances that demonstrate performance improvement are then added to the labeled dataset. Experimental results indicate that the proposed approach effectively enhances classifier performance post-oversampling.