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Vitor Basto-Fernandes, Iryna Yevseyeva & Michael Emmerich (2018). Evolutionary multi-objective scheduling for anti-spam filtering throughput optimization. 29th European Conference on Operational Research.

V. M. Fernandes et al.,  "Evolutionary multi-objective scheduling for anti-spam filtering throughput optimization", in 29th European Conf. on Operational Research, Valencia, 2018

@misc{fernandes2018_1765781098626,
	author = "Vitor Basto-Fernandes and Iryna Yevseyeva and Michael Emmerich",
	title = "Evolutionary multi-objective scheduling for anti-spam filtering throughput optimization",
	year = "2018",
	doi = "ISBN 978-84-09-02938-9",
	howpublished = "Digital",
	url = "http://euro2018valencia.com/"
}

TY  - CPAPER
TI  - Evolutionary multi-objective scheduling for anti-spam filtering throughput optimization
T2  - 29th European Conference on Operational Research
AU  - Vitor Basto-Fernandes
AU  - Iryna Yevseyeva
AU  - Michael Emmerich
PY  - 2018
DO  - ISBN 978-84-09-02938-9
CY  - Valencia
UR  - http://euro2018valencia.com/
AB  - This work presents an evolutionary multi-objective optimization problem formulation for the anti-spam filtering scheduling 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 antispam filtering and the SpamAssassin email dataset. The NSGAII 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 concerning classification quality and classification efficiency.

ER  -