Email Spam Classification Using Hybrid Approach of RBF Neural Network and Particle Swarm Optimization

Email is one of the most popular communication media in the current century; it has become an effective and fast method to share and information exchangeall over the world. In recent years, emails users are facing problem which is spam emails. Spam emails are unsolicited, bulk emails are sent by spammers. It consumes storage of mail servers, waste of time and consumes network bandwidth.Many methods used for spam filtering to classify email messages into two groups spam and non-spam. In general, one of the most powerful tools used for data classification is Artificial Neural Networks (ANNs); it has the capability of dealing a huge amount of data with high dimensionality in better accuracy. One important type of ANNs is the Radial Basis Function Neural Networks (RBFNN) that will be used in this work to classify spam message. In this paper, we present a new approach of spam filtering technique which combinesRBFNN and Particles Swarm Optimization (PSO) algorithm (HC-RBFPSO). The proposed approach uses PSO algorithm to optimize the RBFNN parameters, depending on the evolutionary heuristic search process of PSO. PSO use to optimize the best position of the RBFNN centers c. The Radii r optimize using K-Nearest Neighbors algorithmand the weights w optimize using Singular Value Decomposition algorithm within each iterative process of PSO depending the fitness (error) function. The experiments are conducted on spam dataset namely SPAMBASE downloaded from UCI Machine Learning Repository. The experimental results show that our approach is performed in accuracy compared with other approaches that use the same dataset.