Summary of Online Multi-label Classification Under Noisy and Changing Label Distribution, by Yizhang Zou et al.
Online Multi-Label Classification under Noisy and Changing Label Distribution
by Yizhang Zou, Xuegang Hu, Peipei Li, Jun Hu, You Wu
First submitted to arxiv on: 3 Oct 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 The proposed online multi-label classification algorithm, NCLD, addresses the challenges of noisy labels and concept drift adaptation by simultaneously modeling label scoring and ranking. The convex objective is designed to learn reliable ranking information, which is robust to noisy and changing label distributions. This is achieved through three novel works: local feature graph reconstruction of label scores, unbiased ranking loss, and detection of changes in ground-truth label distribution using the difference between adjacent chunks with unbiased label cardinality. The algorithm also employs efficient and accurate updating based on the closed-form optimal model solution. Experimental results validate the effectiveness of NCLD in classifying instances under noisy and changing label distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to classify things into multiple categories, even when some of the labels are wrong. It’s like trying to guess what kind of animal you have based on its features, but sometimes people might say it’s a rabbit when it’s actually a squirrel! The algorithm is good at adapting to changes in what makes something a certain type of animal, and it works well even with noisy or incorrect information. |
Keywords
* Artificial intelligence * Classification
