2026-05-12T20:23:26.612Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:8720 2024-12-03T20:10:47Z 3

Feature Selection Using Modified Sine Cosine Algorithm with COVID-19 Dataset, Chapter in LNDECT Lecture Notes on Data Engineering and Communications Technologies: ICECMSN 2021: Evolutionary Computing and Mobile Sustainable Networks, Springer, volume 116 2022 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/8720 https://link.springer.com/chapter/10.1007/978-981-16-9605-3_2 M. Zivkovic L. Jovanovic M. Ivanovic A. Krdzic N. Bacanin I. Strumberger The research proposed in this paper shows application of the sine cosine swarm intelligence algorithm for feature selection problem in the machine learning domain. Feature selection is a process that is responsible for selecting datasets’ features that have the biggest effect on the performances and the accuracy of the system. The feature selection task performs the search for the optimal set of features through a enormous search space, and since the swarm intelligence metaheuristics have already proven their performances and established themselves as good optimizers, their application can drastically enhance the feature selection process. This paper introduces the improved version of the sine cosine algorithm that was utilized to address the feature selection problem. The proposed algorithm was tested on ten standard UCL repository datasets and compared to other modern algorithms that have been validated on the same test instances. Finally, the proposed algorithm was tested against the COVID-19 dataset. The obtained results indicate that the method proposed in this manuscript outperforms other state-of-the-art metaheuristics in terms of features number and classification accuracy. bookPart Springer, Singapore 15 31 10.1007/978-981-16-9605-3_2 LNDECT Lecture Notes on Data Engineering and Communications Technologies: ICECMSN 2021: Evolutionary Computing and Mobile Sustainable Networks, volume 116