Supervised and Unsupervised Machine Learning Algorithms: An Empirical Evaluation

Rajarajan Rajkumar, Li Zhang, Vivian Sedov, Kamlesh Mistry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Machine Learning (ML) algorithms are a subset of artificial intelligence that are applied to data with a primary focus of improving its accuracy over time by replicating and imitating the learning styles of human beings. Within this framework, several supervised and unsupervised learning algorithms are studied through different scenarios. The advantages and disadvantages of these algorithms are analysed through these case studies.
Original languageEnglish
Title of host publicationIntelligent Management of Data and Information in Decision Making
Subtitle of host publicationProceedings of the 16th FLINS Conference on Computational Intelligence in Decision and Control & the 19th ISKE Conference on Intelligence Systems and Knowledge Engineering (FLINS-ISKE 2024)
Pages299-306
Number of pages8
ISBN (Electronic)978-981-12-9464-8
DOIs
Publication statusPublished - 30 Jul 2024

Publication series

NameWorld Scientific Proceedings Series on Computer Engineering and Information Science
ISSN (Print)1793-7868
ISSN (Electronic)2972-4465

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