Summary of Detection Of Problem Gambling with Less Features Using Machine Learning Methods, by Yang Jiao et al.
Detection of Problem Gambling with Less Features Using Machine Learning Methods
by Yang Jiao, Gloria Wong-Padoongpatt, Mei Yang
First submitted to arxiv on: 23 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 study in this paper focuses on developing an efficient machine learning model for detecting problem gambling based on user daily actions. The existing datasets provide rich analytic features that can be used to build a robust model. However, considering the complexity and cost of collecting these features in real-world applications, it is essential to reduce the number of features required while maintaining accuracy. To achieve this, the authors propose a deep neural network model called PGN4 that can perform well even with limited analytic features. The study uses two datasets to evaluate the performance of PGN4 and finds that it experiences only a minor drop in performance when reducing the number of features from 102 to 5. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to develop a machine learning model that detects problem gambling based on user behavior. The researchers use existing datasets with rich analytic features to train their model. However, they also want to make sure their model is efficient and doesn’t require too much data. To do this, they test a deep neural network called PGN4 to see if it can still work well even with fewer features. They find that PGN4 does just as well on two different datasets when using only 5 out of the original 102 features. |
Keywords
* Artificial intelligence * Machine learning * Neural network
