Machine Learning for Missing Value Imputation

• URL: http://arxiv.org/abs/2410.08308v1
• Date: Thu, 10 Oct 2024 18:56:49 GMT
• Title: Machine Learning for Missing Value Imputation
• Authors: Abu Fuad Ahmad, Khaznah Alshammari, Istiaque Ahmed, MD Shohel Sayed,
• Abstract summary: The main objective of this article is to conduct a comprehensive and rigorous review, as well as analysis, of the state-of-the-art machine learning applications in Missing Value Imputation. More than 100 articles published between 2014 and 2023 are critically reviewed, considering the methods and findings. The latest literature is examined to scrutinize the trends in MVI methods and their evaluation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent times, a considerable number of research studies have been carried out to address the issue of Missing Value Imputation (MVI). MVI aims to provide a primary solution for datasets that have one or more missing attribute values. The advancements in Artificial Intelligence (AI) drive the development of new and improved machine learning (ML) algorithms and methods. The advancements in ML have opened up significant opportunities for effectively imputing these missing values. The main objective of this article is to conduct a comprehensive and rigorous review, as well as analysis, of the state-of-the-art ML applications in MVI methods. This analysis seeks to enhance researchers' understanding of the subject and facilitate the development of robust and impactful interventions in data preprocessing for Data Analytics. The review is performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique. More than 100 articles published between 2014 and 2023 are critically reviewed, considering the methods and findings. Furthermore, the latest literature is examined to scrutinize the trends in MVI methods and their evaluation. The accomplishments and limitations of the existing literature are discussed in detail. The survey concludes by identifying the current gaps in research and providing suggestions for future research directions and emerging trends in related fields of interest.

Related papers

• Empowering Meta-Analysis: Leveraging Large Language Models for Scientific Synthesis [7.059964549363294]
  This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs) Our research introduces a novel approach that fine-tunes the LLM on extensive scientific datasets to address challenges in big data handling and structured data extraction.
  arXiv  Detail & Related papers  (2024-11-16T20:18:57Z)
• Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques [52.71395121577439]
  This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR) It highlights the impact of predictive modeling, AI-enhanced devices, and real-time data analysis in improving resuscitation outcomes. The paper provides a comprehensive overview, classification, and critical analysis of current applications, challenges, and future directions in this emerging field.
  arXiv  Detail & Related papers  (2024-11-03T18:01:50Z)
• Surveying the MLLM Landscape: A Meta-Review of Current Surveys [17.372501468675303]
  Multimodal Large Language Models (MLLMs) have become a transformative force in the field of artificial intelligence. This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs.
  arXiv  Detail & Related papers  (2024-09-17T14:35:38Z)
• Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey [51.87875066383221]
  This paper introduces fundamental concepts, traditional methods, and benchmark datasets, then examine the various roles Machine Learning plays in improving CFD. We highlight real-world applications of ML for CFD in critical scientific and engineering disciplines, including aerodynamics, combustion, atmosphere & ocean science, biology fluid, plasma, symbolic regression, and reduced order modeling. We draw the conclusion that ML is poised to significantly transform CFD research by enhancing simulation accuracy, reducing computational time, and enabling more complex analyses of fluid dynamics.
  arXiv  Detail & Related papers  (2024-08-22T07:33:11Z)
• SyROCCo: Enhancing Systematic Reviews using Machine Learning [6.805429133535976]
  This paper explores the use of machine learning techniques to help navigate the systematic review process. The application of ML techniques to subsequent stages of a review, such as data extraction and evidence mapping, is in its infancy.
  arXiv  Detail & Related papers  (2024-06-24T11:04:43Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
  This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews. The newly emerging AI-generated literature reviews are also appraised. This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis [0.0]
  Supply chain risk assessment (SCRA) has witnessed a profound evolution through the integration of artificial intelligence (AI) and machine learning (ML) techniques. Previous reviews have outlined established methodologies but have overlooked emerging AI/ML techniques. This paper conducts a systematic literature review combined with a comprehensive bibliometric analysis.
  arXiv  Detail & Related papers  (2023-12-12T17:47:51Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• A survey of machine learning techniques in medical applications [0.0]
  The exponential growth of medical data has surpassed the capacity for manual analysis, prompting increased interest in automated data analysis and processing. ML algorithms, capable of learning from data with minimal human intervention, are particularly well-suited for medical data analysis and interpretation. One significant advantage of ML is the reduced cost of collecting labeled training data necessary for supervised learning.
  arXiv  Detail & Related papers  (2023-02-26T08:43:08Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.