A parallel hybrid approach integrating clonal selection with artificial bee colony for logistic regression in spam email detection

Spam emails are sent to recipients for advertisement and phishing purposes. In either case, it disturbs recipients and reduces communication quality. Addressing this issue requires classifying emails on servers as either spam or ham. Numerous methods have been proposed for this classification task....

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Bibliographic Details
Published in:	Neural computing & applications Vol. 37; no. 27; pp. 22401 - 22419
Main Authors:	Dedeturk, Bilge Kagan, Akay, Bahriye
Format:	Journal Article
Language:	English
Published:	London Springer London 01.09.2025 Springer Nature B.V
Subjects:	Accuracy Algorithms Artificial Intelligence Bees Blacklisting Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Deep learning Efficiency Electronic mail systems Feature selection Image Processing and Computer Vision Machine learning Methods Neural networks Optimization Probability and Statistics in Computer Science Regression analysis Robustness S.I.: Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Spamming Special Issue on Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Success Support vector machines Swarm intelligence Logistic regression Spam filtering Artificial bee colony Email spam detection Clonal selection algorithm
ISSN:	0941-0643, 1433-3058
Online Access:	Get full text
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