TITLE:
Optimizing Feedforward Neural Networks Using Biogeography Based Optimization for E-Mail Spam Identification
AUTHORS:
Ali Rodan, Hossam Faris, Ja’far Alqatawna
KEYWORDS:
Spam, BBO, Multilayer Perceptron, Optimization, Biogeography Based Optimization
JOURNAL NAME:
International Journal of Communications, Network and System Sciences,
Vol.9 No.1,
January
25,
2016
ABSTRACT: Spam e-mail has a significant negative impact on individuals and
organizations, and is considered as a serious waste of resources, time and
efforts. Spam detection is a complex and challenging task to solve. In literature, researchers and
practitioners proposed numerous approaches for automatic e-mail spam
detection. Learning-based filtering is one of the important approaches used for
spam detection where a filter needs to be trained to extract the knowledge that
can be used to detect the spam. In this context, Artificial Neural Networks is
a widely used machine learning based filter. In this paper, we propose the use
of a common type of Feedforward Neural Network called Multi-Layer Perceptron
(MLP) for the purpose of e-mail spam identification, where the weights of this
network model are found using a new nature-inspired metaheuristic algorithm
called Biogeography Based Optimization (BBO). Experiments and results based on
two different spam datasets show that the developed MLP model trained by BBO
gets high generalization performance compared to other optimization methods
used in the literature for e-mail spam detection.