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Blending Firefly and Bayes Classifier for Email Spam Classification
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Abstract
Email spam is a serious worldwide problem which causes problems for almost all computer users. This issue not only affects normal users of the internet, but also causes a huge problem for companies and organizations since it costs a huge amount of money in lost productivity, wasting user’s time and network bandwidth. Recently, various researchers are presented several email spam classification techniques. In this paper, we have developed an efficient technique to classify the email spam using firefly and naïve bayes classifier. Initially, the input email data is given to the feature selection to select the suitable feature for spam classification. The traditional firefly algorithm is taken and the optimized feature space is chosen with the best fitness. Once the best feature space is identified through firefly algorithm, the spam classification is done using the naïve bayes classifier. The results for the spam detection are validated through evaluation metrics namely, sensitivity, specificity, accuracy. For comparative analysis, proposed spam classification is compared with the existing works such as particle swarm optimization and neural network. From the results, our proposed algorithm performed better than PSO algorithm and neural network in terms of accuracy and specificity.
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References
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