[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125620-en":3,"doc-seo-125620-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125620,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","A Comprehensive Study of Groundbreaking Machine Learning Research - Analyzing Highly Cited and Impactful Publications across Six Decades","Machine learning continues to advance as a central area of computer science with strong spillover into many scientific and industrial domains. Understanding the citation landscape of highly referenced work helps reveal key trends, influential authors, and major contributions. This study conducts a comprehensive bibliometric analysis of top-cited ML publications from 1959 to 2022 using citation, co-authorship, keyword, and publication trend methods. Results identify influential papers, collaborative networks, dominant countries, and emerging themes, supporting researchers, policymakers, and practitioners.","A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing Highly Cited and Impactful Publications across Six Decades  \nAbsalom E. Ezugwu 1, *, Japie Greeff2, Yuh-Shan Ho3, *,  \n1Unit for Data Science and Computing, North-West University, 11 Hoffman Street, Potchefstroom, 2520, South Africa  \n2 School of Computer Science and Information Systems, Faculty of Natural and Agricultural Sciences, North-West University, Vanderbijlpark, South Africa  \n3Trend Research Centre, Asia University, No. 500, Lioufeng Road, Wufeng, Taichung 41354, Taiwan  \n*Corresponding Authors ([Absalom.ezugwu@nwu.ac.za](Absalom.ezugwu@nwu.ac.za)  ; [ysho@asia.edu.tw](ysho@asia.edu.tw))  \nAbstract. Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields, thereby driving advancements in other domains of interest. As the field continues to evolve, it is crucial to understand the landscape of highly cited publications to identify key trends, influential authors, and significant contributions made thus far. In this paper, we present a comprehensive bibliometric analysis of highly cited ML publications. We collected a dataset consisting of the top-cited papers from reputable ML conferences and journals, covering a period of several years from 1959 to 2022. We employed various bibliometric techniques to analyze the data, including citation analysis, co-authorship analysis, keyword analysis, and publication trends. Our findings reveal the most influential papers, highly cited authors, and collaborative networks within the machine learning community. We identify popular research themes and uncover emerging topics that have recently gained significant attention. Furthermore, we examine the geographical distribution of highly cited publications, highlighting the dominance of certain countries in ML research. By shedding light on the landscape of highly cited ML publications, our study provides valuable insights  \nfor researchers, policymakers, and practitioners seeking to understand the key developments and trends in this rapidly evolving field.  \nKeywords: Machine learning; ML; bibliometric analysis; web of science core collection  \n1. Introduction  \nThe field of machine learning (ML) has undergone a transformative evolution within the realm of artificial intelligence, bringing about significant changes across numerous industries and scientific domains (Ahmed, Jeon & Piccialli, 2022; Zhong et al., 2021; Ezugwu et al., 2023; Ezugwu et al., 2020). The rapid progress of ML techniques and algorithms has resulted in a proliferation of research publications in this field. Consequently, identifying and analyzing influential and highly cited publications have become crucial amidst the extensive literature available. These publications have made significant contributions to the advancement of ML research and its application in various domains (Kotsiantis, Zaharakis & Pintelas, 2006; Kotsiantis, Zaharakis & Pintelas, 2007; Mahesh, 2022) .  \nThis paper probes into the realm of bibliometric exploration in order to provide insights into the landscape of highly cited and influential publications in the field of ML research. Through the application of bibliometric analysis, our objective is to uncover crucial trends, influential authors, top journals, and significant themes within this dynamic field. This investigation serves not only to offer valuable insights into the progress of ML research but also to assist researchers, practitioners, and decision-makers in identifying seminal works and gaining a deeper understanding of the field's direction. Numerous studies have previously presented bibliometric analyses focusing on specific research areas within machine learning. For instance, De Felice and Polimeni (2020) conducted a study on disease forecasting, while Kim, Lee, and Park (2021) explored the application of ML in mental health research. Yu, Xu, and Wang (2021) investigated research trends in support ve","cbCaitlH8iOxk4FL","https://ap.wps.com/l/cbCaitlH8iOxk4FL","pdf",449753,1,50,"English","en",105,"# Introduction\n## Motivation and importance of bibliometric exploration\n## Bibliometric methods and stakeholder value","[{\"question\":\"What is the main goal of the bibliometric analysis in this study?\",\"answer\":\"To map the landscape of highly cited and high-impact machine learning research by identifying influential publications, authors, journals, themes, and trends across the selected period.\"},{\"question\":\"Which bibliometric techniques are used to analyze highly cited ML papers?\",\"answer\":\"The study applies citation analysis, co-authorship analysis, keyword analysis, and publication trend analysis to evaluate impact, collaborations, and research topics.\"},{\"question\":\"What kinds of insights does the study aim to provide for different stakeholders?\",\"answer\":\"Researchers gain an overview and potential gaps, policymakers and funding agencies can allocate resources strategically, and practitioners can track key advancements, prominent contributors, and influential methodologies.\"}]","A Comprehensive Study of Groundbreaking Machine Learning Research - 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