A hybrid correlation-based deep learning model for email spam classification using fuzzy inference system
Femi Emmanuel Ayo, Lukman Adebayo Ogundele, Solanke Olakunle, Joseph Bamidele Awotunde, Funmilayo Kasali
Abstract
Spam emails are unwanted and unsolicited messages. The major problems in email spam detection methods are low detection rates and a high likelihood of false alarms. This study proposes a hybrid correlation-based deep learning model for email spam classification using a fuzzy inference system. Using a rule-based hybrid feature selection technique, we choose the most crucial features from a preprocessed spam-based dataset. The selected features are then loaded into a deep learning model for spam classification, and fuzzy logic is employed to categorize each spam class into its severity levels to decrease misclassification. Compared with other machine learning methods, the proposed method shows better F1-score results for both test set spambase and test set non-spambase of 96.5% and 96.4%, respectively. Similarly, the developed method shows better accuracy, error rate, and processing time of 94.0017%, 5.9983%, and 0.5 s, respectively. The proposed approach also shows a reduced misclassification based on the fuzzy inference system.