Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
Decision support systems with machine learning can help organizations improve operations and lower costs with more precision and efficiency. This work presents a review of state-of-the-art machine learning algorithms for binary classification and makes a comparison of the related metrics between the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/15/4/130 |
| _version_ | 1827622038180724736 |
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| author | Hugo Silva Jorge Bernardino |
| author_facet | Hugo Silva Jorge Bernardino |
| author_sort | Hugo Silva |
| collection | DOAJ |
| description | Decision support systems with machine learning can help organizations improve operations and lower costs with more precision and efficiency. This work presents a review of state-of-the-art machine learning algorithms for binary classification and makes a comparison of the related metrics between them with their application to a public diabetes and human resource datasets. The two mainly used categories that allow the learning process without requiring explicit programming are supervised and unsupervised learning. For that, we use Scikit-learn, the free software machine learning library for Python language. The best-performing algorithm was Random Forest for supervised learning, while in unsupervised clustering techniques, Balanced Iterative Reducing and Clustering Using Hierarchies and Spectral Clustering algorithms presented the best results. The experimental evaluation shows that the application of unsupervised clustering algorithms does not translate into better results than with supervised algorithms. However, the application of unsupervised clustering algorithms, as the preprocessing of the supervised techniques, can translate into a boost of performance. |
| first_indexed | 2024-03-09T11:16:38Z |
| format | Article |
| id | doaj.art-24cd552bfef24c71bd1e5f06ab77169b |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T11:16:38Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-24cd552bfef24c71bd1e5f06ab77169b2023-12-01T00:29:02ZengMDPI AGAlgorithms1999-48932022-04-0115413010.3390/a15040130Machine Learning Algorithms: An Experimental Evaluation for Decision Support SystemsHugo Silva0Jorge Bernardino1Polytechnic of Coimbra, Institute of Engineering of Coimbra—ISEC, Rua Pedro Nunes, 3030-199 Coimbra, PortugalPolytechnic of Coimbra, Institute of Engineering of Coimbra—ISEC, Rua Pedro Nunes, 3030-199 Coimbra, PortugalDecision support systems with machine learning can help organizations improve operations and lower costs with more precision and efficiency. This work presents a review of state-of-the-art machine learning algorithms for binary classification and makes a comparison of the related metrics between them with their application to a public diabetes and human resource datasets. The two mainly used categories that allow the learning process without requiring explicit programming are supervised and unsupervised learning. For that, we use Scikit-learn, the free software machine learning library for Python language. The best-performing algorithm was Random Forest for supervised learning, while in unsupervised clustering techniques, Balanced Iterative Reducing and Clustering Using Hierarchies and Spectral Clustering algorithms presented the best results. The experimental evaluation shows that the application of unsupervised clustering algorithms does not translate into better results than with supervised algorithms. However, the application of unsupervised clustering algorithms, as the preprocessing of the supervised techniques, can translate into a boost of performance.https://www.mdpi.com/1999-4893/15/4/130machine learningdecision support systemsbig dataclusteringhealthcarehuman resources |
| spellingShingle | Hugo Silva Jorge Bernardino Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems Algorithms machine learning decision support systems big data clustering healthcare human resources |
| title | Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems |
| title_full | Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems |
| title_fullStr | Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems |
| title_full_unstemmed | Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems |
| title_short | Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems |
| title_sort | machine learning algorithms an experimental evaluation for decision support systems |
| topic | machine learning decision support systems big data clustering healthcare human resources |
| url | https://www.mdpi.com/1999-4893/15/4/130 |
| work_keys_str_mv | AT hugosilva machinelearningalgorithmsanexperimentalevaluationfordecisionsupportsystems AT jorgebernardino machinelearningalgorithmsanexperimentalevaluationfordecisionsupportsystems |