Abstract
The increasing volume of unsolicited bulk emails has become a major threat to global security. While a significant amount of research has been carried out in terms of proposing new and better algorithms for email spam detection, relatively less attention has been given to evaluation metrics. Some widely used metrics include accuracy, recall, precision, and F-score. This paper proposes a new evaluation metric based on the concepts of fuzzy logic. The proposed metric, termed μO, combines accuracy, recall, and precision into a multi-criteria fuzzy function. Several possible evaluation rules are proposed. As proof of concept, a preliminary empirical analysis of the proposed scheme is carried out using two models, namely BERT (Bidirectional Encoder Representations from Transformers) and LSTM (Long short-term memory) from the domain of deep learning, while utilizing three benchmark datasets. Results indicate that for the Enron and PU datasets, LSTM produces better results of μO, with the values in the range of 0.88 to 0.96, whereas BERT generates better values of μO in the range of 0.94 to 0.96 for Lingspam dataset. Furthermore, extrinsic evaluation confirms the effectiveness of the proposed fuzzy logic metric.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.