Summary of Overcoming Imbalanced Safety Data Using Extended Accident Triangle, by Kailai Sun et al.
Overcoming Imbalanced Safety Data Using Extended Accident Triangle
by Kailai Sun, Tianxiang Lan, Yang Miang Goh, Yueng-Hsiang Huang
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 paper aims to address the issue of imbalanced datasets in safety analytics, which is a common problem that can lead to inaccurate predictions and management problems. The authors extend the theory of the accident triangle by proposing three oversampling methods based on assigning different weights to samples in the minority class. These methods aim to improve the performance of machine learning algorithms on imbalanced datasets, which are crucial for preventing workplace incidents in high-risk industries like construction and trucking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s main goal is to help prevent workplace accidents by improving safety analytics using machine learning. The problem is that existing studies often have imbalanced datasets, making it hard to predict what will happen next. To fix this, the researchers came up with new ways to weigh the importance of each sample in the data, so that the model can learn better from the minority class. This could lead to better decisions being made by managers, like where to focus safety efforts and when to intervene. |
Keywords
» Artificial intelligence » Machine learning
