Summary of Primary and Secondary Factor Consistency As Domain Knowledge to Guide Happiness Computing in Online Assessment, by Xiaohua Wu and Lin Li and Xiaohui Tao and Frank Xing and Jingling Yuan
Primary and Secondary Factor Consistency as Domain Knowledge to Guide Happiness Computing in Online Assessment
by Xiaohua Wu, Lin Li, Xiaohui Tao, Frank Xing, Jingling Yuan
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper, researchers investigate the use of machine learning (ML) models to compute online assessments of happiness, while ensuring high accuracy and explainability of results. They explore how domain knowledge constraints can be incorporated into ML models to make them more trustworthy. The study demonstrates that by using multiple prediction models with additive factor attributions, primary and secondary relations consistency can be achieved, leading to improved happiness computing accuracy and the discovery of significant happiness factors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Happiness online assessments are a new area of research that affects personal growth and social stability. Scientists use advanced machine learning models to calculate happiness scores while keeping results accurate. However, these models lack important knowledge about how happiness is related. This study aims to fill this gap by exploring how domain knowledge can be added to machine learning models to make them more reliable. The researchers show that this approach improves accuracy and helps identify important factors for decision-making. |
Keywords
* Artificial intelligence * Machine learning
