[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125996-en":3,"doc-seo-125996-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125996,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Classical Machine Learning - Seventy Years of Algorithmic Learning Evolution - Research Paper","Machine learning (ML) has reshaped many fields, making foundational understanding essential for sustained progress. This paper surveys major classical ML algorithms and studies twelve decades of state-of-the-art publications via extensive bibliometric analysis. Highly cited conference and journal papers are examined using citation, co-authorship, and keyword techniques to derive influential authors and works, collaboration network evolution, dominant themes, emerging research foci, and geographic distribution, with emphasis on the Global South.","RESEARCH PAPER  \nClassical Machine Learning: Seventy Years of Algorithmic Learning Evolution  \nAbsalom E. Ezugwu 1*, Yuh-Shan Ho2*, Ojonukpe S. Egwuche 1, Olufisayo S. Ekundayo 1, Annette Van Der Merwe3, Apu K. Saha4, Jayanta Pal5  \n1 Unit for Data Science and Computing, North-West University, 11 Hoffman Street, Potchefstroom 2520, South Africa  \n2 Trend Research Centre, Asia University, No. 500, Lioufeng Road, Taichung 41354, Taiwan  \n3 School of Computer Science and Information Systems, North-West University, 11 Hoffman Street, Potchefstroom 2520, South Africa  \n4 Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura, 799046, India  \n5 Department of IT, Tripura University, Suryamaninagar, Tripura 799022, India  \n*Corresponding author: Absalom E. Ezugwu (Email: [absalom.ezugwu@nwu.ac.za](absalom.ezugwu@nwu.ac.za) ; ORCID: 0000-0002-3721- 3400)；Yuh-Shan Ho ([Email:](Email: ysho@asia.edu.tw)[ ](Email: ysho@asia.edu.tw)[ysho@asia.edu.tw](Email: ysho@asia.edu.tw); ORCID: 0000-0002-2557-8736)  \nKeywords: Machine learning; classic machine learning; bibliometric analysis; perceptron, random forests, decision trees, linear regression, logistic regression, support vector machines.  \nSubmitted: May 19, 2023; Revised: Jun 17, 2024; Accepted: Jul 15, 2024  \nAccepted for publication in: Data Intelligence | MIT Press  \nAbstract. Machine learning (ML) has transformed numerous fields, but understanding its foundational research is crucial for its continued progress. This paper presents an overview of the major classical ML algorithms and examines the state-of-the-art publications, spanning twelve decades, through an extensive bibliometric analysis study. We analyzed a dataset of highly cited papers from prominent ML conferences and journals, employing techniques such as citation and keyword analyses to uncover key insights. The study further identifies the most influential papers and authors, reveals the evolving collaborative networks within the ML community, and pinpoints prevailing research themes and emerging areas of focus. Additionally, we examine the geographic distribution of highly cited publications, highlighting the leading countries in ML research. This study provides a comprehensive overview of the evolution of traditional learning algorithms, and their impacts, and discusses challenges and opportunities for future development, with a particular focus on the Global South. The findings from this paper offer valuable insights for both ML experts and the broader research community, enhancing understanding of the field's trajectory and its significant influence on recent advances in learning algorithms.  \n1. Introduction  \nMachine Learning (ML), which is a subfield of artificial intelligence (AI), has exponentially grown into a transformative force, changing a multitude of industries and fundamentally altering the way we approach and solve complex real-world problems. Over the past few decades, the field of ML has evolved at an exponential pace, and its impact on our society is undeniable. ML algorithms and their variants implementation techniques have become integral to a wide range of applications, from healthcare, finance, engineering, and manufacturing to recommendation systems, autonomous vehicles, and natural language processing [1, 2] . This transformative technology has not only reshaped industries and several government critical sectors but has also changed the way we perceive and interact with our increasingly data-driven world [3] .  \nMoreover, the rapid progress in ML has been driven by a plethora of influential publications, ranging from groundbreaking publications to innovative inventions by inspiring authors. This progress has fostered  \ncollaborative networks that have spurred diverse innovation globally. The foundational knowledge encapsulated within these classic ML publications has provided the building blocks for contemporary research and technological advancements [4, 5, 6] . Un","cbCaidqJ8SYsUyr0","https://ap.wps.com/l/cbCaidqJ8SYsUyr0","pdf",1631065,4,1,42,"English","en",105,"# Introduction\n## Growth and significance of machine learning\n## Motivation for bibliometric review\n## Dataset and bibliometric methods","[{\"question\":\"What is the main goal of this paper on classical machine learning?\",\"answer\":\"To overview major classical ML algorithms and analyze twelve decades of influential publications using bibliometric methods, revealing research themes, authors, networks, and geographic trends.\"},{\"question\":\"What dataset and techniques are used for the bibliometric analysis?\",\"answer\":\"The study uses highly cited papers from reputable ML conferences and journals and applies citation analysis, co-authorship analysis, keyword analysis, and publication trend examination.\"},{\"question\":\"What types of insights does the study aim to produce?\",\"answer\":\"It identifies the most influential papers and authors, shows how collaboration networks evolve, pinpoints prevailing and emerging research themes, and highlights leading countries in ML research.\"}]","Classical Machine Learning - 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