Analysis of e-Mail Spam Detection Using a Novel Machine Learning-Based Hybrid Bagging Technique

Abstract

e-mail service providers and consumers find it challenging to distinguish between spam and nonspam e-mails. The purpose of spammers is to spread false information by sending annoying messages that catch the attention of the public. Various spam identification techniques have been suggested and evaluated in the past, but the results show that the more research in this regard is required to enhance accuracy and to reduce training time and error rate. Thus, this research proposes a novel machine learning-based hybrid bagging method for e-mail spam identification by combining two machine learning methods: random forest and J48 (decision tree). The proposed framework categorizes the e-mail into ham and spam. The database is split into multiple sets and provided as input to each method in this procedure. Moreover, tokenization, stemming, and stop word removal are performed in the preprocessing stage. Further, correlation feature selection (CFS) is employed in this research to select the required features from the preprocessed data. The effectiveness of the presented method is evaluated in terms of true-negative rates, accuracy, recall, precision, false-positive rate, f-measure, and false-negative rate; the outcomes of three studies are compared. According to the results, the presented hybrid bagged model-based SMD technology achieved 98 percent accuracy.

A systematic review of prediction accuracy as an evaluation measure for determining machine learning model performance in healthcare systems

“…Related to improving prediction accuracy is the issue of improving training time and reducing prediction error rate. ML based hybrid bagging technique application [48] using random forest and decision tree (J48)…”

Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems

“…Comparative performance evaluation to improve prediction accuracy [ 46 ] of two ML models; support vector machine and random forest for the detection of junk mail spam showed prediction accuracy of models as; Support vector machine 93.52% and Random forest 91.41%.Related to improving prediction accuracy is the issue of improving training time and reducing prediction error rate. ML based hybrid bagging technique application [ 47 ] using random forest and decision tree (J48) for the analysis of email spam detection showed 98% prediction accuracy score. Other performance metrics evaluated include true negative rates, false positive rate and false negative rate, precision, recall and f-measure (f1-score).…”

Building an Effective Email Spam Classification Model with spaCy

“…Users of the United Nations, for example, have been victims of this type of attack [7]. If users of email servers recognise a message as spam, it is better not to click on any links or attachments [8]. Spammers sometimes add unsubscribe or unsubscribe links to verify that your email address is active; these types of links usually steal information, so users should not click on them [7], [8].…”

“…If users of email servers recognise a message as spam, it is better not to click on any links or attachments [8]. Spammers sometimes add unsubscribe or unsubscribe links to verify that your email address is active; these types of links usually steal information, so users should not click on them [7], [8]. Spam messages are difficult to stop because they can be sent through botnets, which are networks of pre-infected computers that make it difficult to track and stop the original spam [9].…”