2026-04-17T17:41:52.570Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:8982 2022-07-02T21:26:15Z 3

Support Vector Machine Performance Improvements by Using Sine Cosine Algorithm, Chapter in LNDECT Lecture Notes on Data Engineering and Communications Technologies: CIS 2021: Congress on Intelligent Systems, Springer, volume 114 2022 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/8982 https://link.springer.com/chapter/10.1007/978-981-16-9416-5_58 M. Zivkovic N. Vukobrat A. Chhabra T. A. Rashid K. Venkatachalam N. Bacanin The optimization of parameters has a crucial influence on the solution efficacy and the accuracy of the support vector machine (SVM) in the machine learning domain. Some of the typical approaches for determining the parameters of the SVM consider the grid search approach (GS) and some of the representative swarm intelligence metaheuristics. On the other side, most of those SVM implementations take into the consideration only the margin, while ignoring the radius. In this paper, a novel radius–margin SVM approach is implemented that incorporates the enhanced sine cosine algorithm (eSCA). The proposed eSCA-SVM method takes into the account both maximizing the margin and minimizing the radius. The eSCA has been used to optimize the penalty and RBF parameter in SVM. The proposed eSCA-SVM method has been evaluated against four binary UCI datasets and compared to seven other algorithms. The experimental results suggest that the proposed eSCA-SVM approach has superior performances in terms of the average classification accuracy than other methods included in the comparative analysis. bookPart Springer, Singapore 791 803 10.1007/978-981-16-9416-5_58 LNDECT Lecture Notes on Data Engineering and Communications Technologies: CIS 2021: Congress on Intelligent Systems, volume 114