Summary of Machine Learning For Missing Value Imputation, by Abu Fuad Ahmad et al.
Machine Learning for Missing Value Imputation
by Abu Fuad Ahmad, Khaznah Alshammari, Istiaque Ahmed, MD Shohel Sayed
First submitted to arxiv on: 10 Oct 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 This paper conducts a comprehensive review of machine learning (ML) applications in Missing Value Imputation (MVI) methods, analyzing over 100 articles published between 2014 and 2023. The study aims to enhance researchers’ understanding of MVI and facilitate the development of robust interventions in data preprocessing for Data Analytics. The review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique, examining trends in MVI methods and their evaluation. The paper discusses the accomplishments and limitations of existing literature, identifying current gaps and suggesting future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to fix missing data values using machine learning. It’s like trying to fill in the blanks on a puzzle! Researchers are always working to improve this process, so they can make better predictions and decisions from the data. In this paper, they review over 100 recent studies on this topic, looking for trends and patterns. They want to help others understand what works well and what doesn’t, so they can make even better progress in the future. |
Keywords
* Artificial intelligence * Machine learning
