Application of Natural Language Processing and Machine Learning Boosted with Swarm Intelligence for Spam Email Filtering

Nebojsa Bacanin, Miodrag Zivkovic, Catalin Stoean (), Milos Antonijevic, Stefana Janicijevic, Marko Sarac and Ivana Strumberger
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Nebojsa Bacanin: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Miodrag Zivkovic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Catalin Stoean: Human Language Technologies Center, Faculty of Mathematics and Computer Science, University of Bucharest, Academiei 14, 010014 Bucharest, Romania
Milos Antonijevic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Stefana Janicijevic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Marko Sarac: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Ivana Strumberger: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia

Mathematics, 2022, vol. 10, issue 22, 1-31

Abstract: Spam represents a genuine irritation for email users, since it often disturbs them during their work or free time. Machine learning approaches are commonly utilized as the engine of spam detection solutions, as they are efficient and usually exhibit a high degree of classification accuracy. Nevertheless, it sometimes happens that good messages are labeled as spam and, more often, some spam emails enter into the inbox as good ones. This manuscript proposes a novel email spam detection approach by combining machine learning models with an enhanced sine cosine swarm intelligence algorithm to counter the deficiencies of the existing techniques. The introduced novel sine cosine was adopted for training logistic regression and for tuning XGBoost models as part of the hybrid machine learning-metaheuristics framework. The developed framework has been validated on two public high-dimensional spam benchmark datasets (CSDMC2010 and TurkishEmail), and the extensive experiments conducted have shown that the model successfully deals with high-degree data. The comparative analysis with other cutting-edge spam detection models, also based on metaheuristics, has shown that the proposed hybrid method obtains superior performance in terms of accuracy, precision, recall, f1 score, and other relevant classification metrics. Additionally, the empirically established superiority of the proposed method is validated using rigid statistical tests.

Keywords: machine learning; spam detection; natural language processing; metaheuristics algorithm; swarm intelligence; artificial intelligence; sine cosine algorithm; optimization; classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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