Abstract
This paper presents an evolutionary multi-objective optimization problem formulation for the anti-spam filtering problem, addressing both the classification quality criteria (False Positive and False Negative error rates) and email messages classification time (minimization). This approach is compared to single objective problem formulations found in the literature, and its advantages for decision support and flexible/adaptive anti-spam filtering configuration is demonstrated. A study is performed using the Wirebrush4SPAM framework anti-spam filtering and the SpamAssassin email dataset. The NSGA-II evolutionary multi-objective optimization algorithm was applied for the purpose of validating and demonstrating the adoption of this novel approach to the anti-spam filtering optimization problem, formulated from the multi-objective optimization perspective. The results obtained from the experiments demonstrated that this optimization strategy allows the decision maker (anti-spam filtering system administrator) to select among a set of optimal and flexible filter configuration alternatives with respect to classification quality and classification efficiency.
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Acknowledgements
SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure.
Funding: This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia) and FEDER (European Union). This work was partially supported by the project Platform of integration of intelligent techniques for analysis of biomedical information (TIN2013-47153-C3-3-R) from the Spanish Ministry of Economy and Competitiveness.
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Ruano-Ordás, D., Basto-Fernandes, V., Yevseyeva, I., Méndez, J.R. (2017). Evolutionary Multi-objective Scheduling for Anti-Spam Filtering Throughput Optimization. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_12
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