Abstract
Spam exists in several domains including SMS and Emails which are usually targeted by spammers to steal personal information, money, data, etc. There are several models exist for SMS and Email spam detection out of which supervised learning-based model is mostly efficient. However, the comprehensive study for spam detection with consideration to multiple domains simultaneously, is missing. In this paper an experimental evaluation on the effect of using feature selection techniques in the domain of SMS and Email spam detection is conducted. Parameters such as ROC and Train/Test time along with common parameters are used to evaluate performance of spam detection models. The experimentation results shows that the choice of feature selection technique has profound effect on the performance of the spam detection model and can be seen in the result generated using different evaluation measures out of which some are not used in the both domains previously.
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Author’s are thankful to Shri G. S. Institute of Technology and Science, Indore for providing all facilities and necessary support to carry out the research work.
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Chaturvedi, S.A., Purohit, L. (2023). Feature Selection-Based Spam Detection System in SMS and Email Domain. In: Shakya, S., Du, KL., Ntalianis, K. (eds) Sentiment Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1432. Springer, Singapore. https://doi.org/10.1007/978-981-19-5443-6_4
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DOI: https://doi.org/10.1007/978-981-19-5443-6_4
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