# Hyperparameter Optimization of Ensemble Models for Spam Email Detection
## 2 Related Work
The naive bayes algorithm was used for the probabilistic classification of emails in the Eron1 dataset, and the Hidden Markov Field was utilized to model the statistical behavior of spam. Feature vectors for incoming messages were created by breaking down the emails into features and weighting them to consider inter-word dependence in the learning algorithms. The hybrid approach was able to address the weaknesses of both individual algorithms and demonstrated high efficiency in terms of accuracy and time consumption for spam detection. The hybrid model was compared to the NB and MRF models and was found to have more accurate predictions and faster convergence, with an execution time of 1500 ms compared to 2750 ms and 7500 ms for the NB and MRF models, respectively. In this study [25], a novel method for identifying and blocking spam messages sent via Short Message Service (SMS) using a discrete Hidden Markov Model (HMM) that incorporates weighted features was presented. Traditional methods such as Rule-Based Systems (RBS) and Content-Based Filtering (CBF) have shortcomings and newer, more complex hybrid models struggle with performance and speed. A previous study found that the HMM method proposed in this paper is comparable to deep-learning techniques. To enhance the performance, a new approach of weighting and labelling words in SMS messages to format the observation sequence within the HMM is presented. The experiments on open datasets from the University of California, Irvine, shows the new HMM method with weighted features to have improved accuracy, faster training, and filtering speed. Results show that it outperforms the LSTM and is close to CNN in terms of classification accuracy. This method was also evaluated on a Chinese SMS dataset which further reinforced its accuracy and speed. In this study [26], the effectiveness of 45 different algorithms was tested on a dataset of 1017 emails collected from various Gmail and Hotmail accounts. The goal was to classify the emails as spam or non-spam using the Weka program. The naive Bayes multinomial and naive Bayes multinomial updateable algorithms had the highest classification success rate at 94.7886%, followed by the random forest trees algorithm at 93.6087%, Meta. Multi-Class Classifier and Functions SGD at 92.4287%, Functions SMO at 91.7404%, Meta Random Committee at 91.0521%, Bayes naive Bayes at 90.3638%, and Bayes naive Bayes updateable at 90.3638%. The Genetic Decision Tree Processing with Natural Language Processing (GDTPNLP) was proposed by [10] for detecting text-enabled and voice-enabled spam emails. The GDTPNLP, after genetic analysis of incoming emails, assigns a confidence threshold to the emails and compares the value with the trained set. A higher confidence threshold indicates spam, and a lower threshold indicates legitimate emails. The hybrid model's performance was compared to other learning algorithms such as NB, SVM, Nearest Neighbour, and J48. The GDTPNLP achieved an accuracy of 98.6% while NB, SVM, NN, and J48 achieved 80%, 90%, 89%, and 89.7% accuracies, respectively. This study is quite distinct as it considered voice-enabled email. The approach is promising. This study [27] presented a new method called \"category-learning attention\" that aims to enhance the performance of machine translation. Unlike the standard approach, which only weighs words within the same text, the proposed method utilizes category-level features, specially designed for short texts, by applying a category differentiation matrix to identify words that are heavily distributed within the same category. This method was further developed into two different variations: \"category-learning scaled-dot-product attention\" and \"category-learning multi-head attention (CL-MHA) mechanisms.\" These mechanisms were integrated into a bidirectional gate recurrent unit (Bi-GRU) model, which was evaluated using the SMS spam collection dataset from the University of California, Irvine.