Abstract
It is very normal for any user to receive hundreds of emails every day. Almost 93% of them are spam messages which include mainly advertisements from the industries like software, phishing, gambling, stocks, electronics, pharmaceutical, loan, and malware attempts etc. Spams messages not only waste user’s time but also eats up user valuable space. In this paper, a nature inspired metaheuristics technique has been used for email classification which emphasizes on reducing false-positive problem of treating spam messages as ham. It uses metaheuristics-based feature selection methods and employs extra-tree classifier to classify emails into spam and ham. The proposed model has accuracy of 95.5%, specificity of 93.7%, and F1-score of 96.3%, which is clearly a major improvement over the previous researches which have been conducted in this field using decision trees. The comparative analysis of extra-tree classifiers with other classifiers like decision trees and random forest has also been studied.
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Sharaff, A., Gupta, H. (2019). Extra-Tree Classifier with Metaheuristics Approach for Email Classification. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 924. Springer, Singapore. https://doi.org/10.1007/978-981-13-6861-5_17
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DOI: https://doi.org/10.1007/978-981-13-6861-5_17
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