Classical Machine Learning: Seventy Years of Algorithmic Learning Evolution

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 significant 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 citation and keyword analyses to uncover critical 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 focus areas. 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. It discusses challenges and opportunities for future development, focusing 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.

A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing Highly Cited and Impactful Publications across Six Decades

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 for researchers, policymakers, and practitioners seeking to understand the key developments and trends in this rapidly evolving field.

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent.