Summary of A Similarity-based Oversampling Method For Multi-label Imbalanced Text Data, by Ismail Hakki Karaman et al.
A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data
by Ismail Hakki Karaman, Gulser Koksal, Levent Eriskin, Salih Salihoglu
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers tackle the problem of obtaining labeled data for machine learning projects in real-world applications. They introduce a novel oversampling method for multi-label text classification that addresses data imbalance issues by identifying potential new samples from unlabeled data and evaluating their contribution to classifier performance enhancement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This approach helps to improve classifier performance post-oversampling, making it a valuable tool for machine learning practitioners working on multi-label classification tasks. The study demonstrates the effectiveness of this method in enhancing classifier performance, which is essential for many applications such as natural language processing and information retrieval. |
Keywords
» Artificial intelligence » Classification » Machine learning » Natural language processing » Text classification
