Abstract
Bandwidth, time, and storage space are the major three assets in computational world. Spam emails affect all the three, thus degrade the overall efficiency of the system. Spammers are using new tricks and traps to land these frivolous mails into our inbox. To make mailboxes more intelligent, our effort will be to devise a new algorithm that will help to classify emails in much smarter and efficient way. This paper analyzes various spam classification techniques and thereby put forward a new way of classifying spam emails. This paper thoroughly compares the results that various authors have got while simulating their architectures. Our approach of classification works efficiently and more accurately on varied length and type of datasets during training and testing phases. We tried to minimize the error ratio and increase classifier efficiency by implementing Genetic Algorithm concept.
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Shah, N.F., Kumar, P. (2018). A Comparative Analysis of Various Spam Classifications. In: Sa, P., Sahoo, M., Murugappan, M., Wu, Y., Majhi, B. (eds) Progress in Intelligent Computing Techniques: Theory, Practice, and Applications. Advances in Intelligent Systems and Computing, vol 719. Springer, Singapore. https://doi.org/10.1007/978-981-10-3376-6_29
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DOI: https://doi.org/10.1007/978-981-10-3376-6_29
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