aspg-18-2429-1792025626 20261015005346 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2024 11 2 Alaa Abdulazeez Qanbar Department of Statistics and Informatics, University of Mosul, Mosul, Iraq Zakariya Yahya Algamal Department of Statistics and Informatics, University of Mosul, Mosul, Iraq A significant proportion of one type of pattern and a relatively small quantity of another type of pattern can be found in many unbalanced real data sets. In addition, finding significant observations and excluding influential observations is effectively accomplished through diagnostic analysis. Support vector machines (SVM), a common classification technique, perform poorly on imbalanced datasets and when influential observations exist. In this research, the pigeon optimization algorithm as a metaheuristic algorithm is employed to address the influence observation issues in SVM. Experiments are done on three real sets of data. Our approach provides higher classification accuracy compared to other widely used algorithms. This approach could be used for further biological, chemical, and medical datasets. 2024 22 29 2429 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.110202 https://americaspg.com/journal/18/article/2429 https://americaspg.com/storage/21705691173.pdf