article · International Journal of Computer and Information Technology(2279-0764)
This study explored current research methods, emerging themes, and their implications within machine learning. Researchers conducted a content analysis of 100 articles published in IEEE journals since 2019. The findings indicate that machine learning research primarily employs quantitative methods with an experimental design. It was observed that researchers often use multiple algorithms to solve problems and prioritise optimal feature selection to enhance performance. While confusion matrices remain key for evaluating algorithm performance, processing time is also gaining importance. Python and its libraries are the most common tools. Frequently used algorithms for classification and prediction include Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree.
Understanding the prevailing research methods, tools, and algorithms in machine learning is crucial for guiding future studies and development. This insight helps researchers and practitioners identify effective approaches and emerging trends, potentially leading to more robust and efficient machine learning applications across various sectors.
This research provides an overview of common practices and tools in machine learning research. It does not describe a specific application pathway, a particular user, or indicate a readiness level for commercialisation. The findings are more relevant for guiding academic research and development strategies within the field.
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Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.
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DOI: 10.24203/ijcit.v10i2.79
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