# Hyperparameter Optimization of Ensemble Models for Spam Email Detection
## 2 Related Work  
In recent times, the machine-learning approach to spam email detection has continued to increase in addition to other spam-filtering techniques such as list-based (Whitelist, Greylist, and Real-time blacklist) and word-based (Heuristic filters, word-based: DNS lookup), challenge-response, and so on. Several studies have applied machine and deep learning with the intent of improving the performance of spam filters for classifying emails. In this study [16], a technique for detecting spam emails was introduced. This method utilizes a decision-tree-mining approach and focuses on the email header rather than the entire content of the email. An incremental learning algorithm based on the C4.5 decision tree algorithm is also used to improve the technique's ability to adapt to changes in the structure of spam. The model achieved a precision rate of 96%. This study [17] distinguished between two types of spam emails: complete spam, which is spam that is considered spam by all users, and semi-spam, which is spam that is considered spam by some users but not by others. They developed a method for identifying spam that combines Bayesian filtering for complete spam with a crowdsourcing mechanism for identifying semi-spam. The crowdsourcing aspect of the method involves soliciting reports of spam from contacts or credible users with similar interests. The authors achieved an accuracy rate of 95.1% and believed that their model's performance could be improved by applying the concept of virtual credits to stimulate self-centered nodes to report spam and by enhancing connectivity and throughput. This study [18] presented an email-filtering approach that utilizes semantic methods, specifically the WordNet ontology, to classify emails as either spam or non-spam. The approach aims to reduce the high dimensionality of email text by applying semantic methods and similarity measures, and then further reduces the number of features through the use of feature-selection techniques such as Principal Component Analysis (PCA) and Correlation Feature Selection (CFS). The proposed approach was tested on the Enron dataset and was found to have a high accuracy rate of above 90% when using the logistic regression classification algorithm, with a reduction in the number of features by over 90%. The proposed method was also found to have a higher accuracy rate and faster performance compared to other related approaches. The study by [19] combined Particle Swarm Optimization (PSO) with naive Bayes (NB) to create a new model for classifying emails as spam or non-spam. The model was trained using 1000 emails from the Ling dataset, and features were selected from the bag of words using Correlation-based Feature Selection (CFS). The performance of the new NB and PSO model was compared to that of the ordinary NB model, and it was found that the NB and PSO model had a greater performance for all evaluation metrics (precision, recall, F-measure, and accuracy), with values above 94%, while the ordinary NB model had values below 89% for all metrics. This study [20] carried out a systematic review of several machine-learning applications and their performances in spam detection. The current trends and open research areas in spam filtering were discussed extensively. The strength and weaknesses of the algorithms, such as the Bayesian classification, random forest, ANNs, SVMs, deep learning, Artificial Immune Systems, and Rough sets, amongst others, were compared. They verified that significant progress has been made and more is required in the struggle to end spamming. Finally, they recommended machine-, deep-, and deep-adversarial-learning algorithms as possible future technology for the effective management of spam emails. This study [21] investigated various classification-based data-mining techniques such as the J48 decision tree, random forest, naive Bayes, and SMO for identifying spam emails and analyzing their performance on a spam dataset.