# Imbalanced Multi-label Classification for Business-related Text with Moderately Large Label Spaces

arXiv: https://arxiv.org/abs/2306.07046 · HTML: https://mlsanity.com/paper/2306.07046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Comparing BERT against traditional machine learning text classification](https://mlsanity.com/paper/2005.13012/) (2020)
- [Comprehensive Comparative Study of Multi-Label Classification Methods](https://mlsanity.com/paper/2102.07113/) (2021)
- [Classifier Chains: A Review and Perspectives](https://mlsanity.com/paper/1912.13405/) (2019)

## Abstract (arXiv)

In this study, we compared the performance of four different methods for multi label text classification using a specific imbalanced business dataset. The four methods we evaluated were fine tuned BERT, Binary Relevance, Classifier Chains, and Label Powerset. The results show that fine tuned BERT outperforms the other three methods by a significant margin, achieving high values of accuracy, F1 Score, Precision, and Recall. Binary Relevance also performs well on this dataset, while Classifier Chains and Label Powerset demonstrate relatively poor performance. These findings highlight the effectiveness of fine tuned BERT for multi label text classification tasks, and suggest that it may be a useful tool for businesses seeking to analyze complex and multifaceted texts.