{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:14:11Z","timestamp":1760116451594,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In data science and machine learning, efficient and scalable algorithms are paramount for handling large datasets and complex tasks. Classification algorithms, in particular, play a crucial role in a wide range of applications, from image recognition and natural language processing to fraud detection and medical diagnosis. Traditional classification methods, while effective, often struggle with scalability and efficiency when applied to massive datasets. This challenge has driven the development of innovative approaches that leverage modern computational frameworks and parallel processing capabilities. This paper presents the Bison Algorithm, applied to classification problems. The algorithm, inspired by the social behavior of bison, aims to enhance the accuracy of classification tasks. The Bison Algorithm is implemented using PySpark, leveraging the distributed computing power to handle large datasets efficiently. This study compares the performance of the Bison Algorithm on several dataset sizes using speedup and scaleup as the performance measure.<\/jats:p>","DOI":"10.3390\/a17110501","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T09:52:54Z","timestamp":1730713974000},"page":"501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Parallelization of the Bison Algorithm Applied to Data Classification"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8419-0192","authenticated-orcid":false,"given":"Simone A.","family":"Ludwig","sequence":"first","affiliation":[{"name":"Department of Computer Science, North Dakota State University, Fargo, ND 58105, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7236-0433","authenticated-orcid":false,"given":"Jamil","family":"Al-Sawwa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tafila Technical University, P.O. Box 179, Tafila 66110, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0815-4065","authenticated-orcid":false,"given":"Aaron Mackenzie","family":"Misquith","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Dakota State University, Fargo, ND 58105, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"ref_1","unstructured":"Koopmans, T.C. (1951). Maximization of a Linear Function of Variables Subject to Linear Inequalities, Wiley and Chapman-Hall. Activity Analysis of Production and Allocation."},{"key":"ref_2","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley."},{"key":"ref_3","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Holland, J.H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press. [2nd ed.].","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dorigo, M., and St\u00fctzle, T. (2004). Ant Colony Optimization, MIT Press.","DOI":"10.7551\/mitpress\/1290.001.0001"},{"key":"ref_6","unstructured":"Silhavy, R. (2019). Performance of the Bison Algorithm on Benchmark IEEE CEC 2017. Artificial Intelligence and Algorithms in Intelligent Systems, CSOC2018, Springer. Advances in Intelligent Systems and Computing."},{"key":"ref_7","first-page":"233","article-title":"Parallel Genetic Algorithms Using MPI","volume":"12","author":"Doe","year":"2015","journal-title":"J. Comput. 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