Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 2 , Issue 1 , PP: 08-15, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Doaa S. Khafaga 1 * , Hussein Alkattan 2 , Alhumaima A. Subhi 3
Doi: https://doi.org/10.54216/JAIM.020101
When there are numerous possible solutions for a given class in a given problem, majority voting or plurality voting is typically employed. One common technique for improving classification accuracy is bagging, which involves training many classifiers on slightly different datasets and then voting on the combined results. In this research, we examine how alternative voting procedures affect the efficiency of two distinct classification algorithms applied to datasets of varying complexity. Despite the increased computing cost associated with determining preference order, the results show that the single transferable vote can be a suitable alternative to plurality voting.
Zoo data , Voting classifier , Support vector manchines , Neural networks , Decision trees.
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