Year: 2024
Type: article
Abstract: 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.
Source: Algorithms
DOI: 10.3390/a17110501
Institutions: North Dakota State University, Tafila Technical University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,819
Awards: G1453181110, Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, G3562386289, G4010157413, ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI), Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G5367127336, G5464919783, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI
OpenAlex ID: W4404040532