Evolutionary Multi-objective Scheduling for Anti-Spam Filtering Throughput Optimization

Date

2017-06-02

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Springer

Type

Article

Peer reviewed

Yes

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.

Description

Keywords

Rule-based anti-spam systems Scheduling Multi-objective optimization

Citation

Ruano-Ordás D., Basto-Fernandes V., Yevseyeva I., Mendez J.R., Evolutionary Multi-objective Scheduling for Anti-Spam Filtering Throughput Optimization. In: Martínez de Pisón F.J., Urraca R., Quintián H., Corchado E. Hybrid Artificial Intelligent Systems, HAIS2017, Le Rjoha, Spain, June 21-23, Lecture Notes on Artificial Intelligence, vol. 10334, Springer, pages 137-148, 2017, doi:10.1007/978-3-319-59650-1_12

Rights

Research Institute