2026-05-10T08:01:13.419Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:9263 2023-02-09T13:03:24Z 3

Training Logistic Regression Model by Hybridized Multi-verse Optimizer for Spam Email Classification, Chapter in LNNS Lecture Notes in Networks and Systems: ICDSA 2022: Proceedings of International Conference on Data Science and Applications, Springer, volume 552 2023 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/9263 https://link.springer.com/chapter/10.1007/978-981-19-6634-7_35 M. Zivkovic A. Petrovic N. Bacanin M. Djuric A. Vesic I. Strumberger M. Marjanovic Spam emails pose a significant threat to end users, annoying them and wasting their time. To counter this problem, numerous spam detection systems have been proposed recently, where the most of the solutions have grounds in the machine learning algorithms, due to their efficiency in classification tasks. Unfortunately, existing spam detection solutions typically face low detection rate and generally have troubles in dealing with high-dimensional data. To address this problem, this paper suggests a hybrid spam detection approach by combining the logistic regression classifying model with the hybridized multi-verse optimizer swarm intelligence metaheuristics. The proposed approach was validated on a public benchmark dataset (CSDMC2010) and compared to other cutting-edge techniques. The obtained results indicate that the suggested hybrid approach outperforms other spam detection solutions included in the comparative analysis, by achieving the highest classification accuracy. bookPart Springer, Singapore 507 520 10.1007/978-981-19-6634-7_35 LNNS Lecture Notes in Networks and Systems: ICDSA 2022: Proceedings of International Conference on Data Science and Applications, volume 552