eprintid: 16579
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/65/79
datestamp: 2023-12-19 03:23:05
lastmod: 2023-12-19 03:23:05
status_changed: 2023-12-19 03:06:31
type: article
metadata_visibility: show
creators_name: Khan, S.A.
creators_name: Iqbal, K.
creators_name: Mohammad, N.
creators_name: Akbar, R.
creators_name: Ali, S.S.A.
creators_name: Siddiqui, A.A.
title: A Novel Fuzzy-Logic-Based Multi-Criteria Metric for Performance Evaluation of Spam Email Detection Algorithms
ispublished: pub
note: cited By 4
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 (Formula presented.), 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 (Formula presented.), with the values in the range of 0.88 to 0.96, whereas BERT generates better values of (Formula presented.) in the range of 0.94 to 0.96 for Lingspam dataset. Furthermore, extrinsic evaluation confirms the effectiveness of the proposed fuzzy logic metric. © 2022 by the authors.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137367782&doi=10.3390%2fapp12147043&partnerID=40&md5=073e00f8cb1bf6c9ecc8cd7ff2a27799
id_number: 10.3390/app12147043
full_text_status: none
publication: Applied Sciences (Switzerland)
volume: 12
number: 14
refereed: TRUE
issn: 20763417
citation:   Khan, S.A. and Iqbal, K. and Mohammad, N. and Akbar, R. and Ali, S.S.A. and Siddiqui, A.A.  (2022) A Novel Fuzzy-Logic-Based Multi-Criteria Metric for Performance Evaluation of Spam Email Detection Algorithms.  Applied Sciences (Switzerland), 12 (14).   ISSN 20763417