# Hyperparameter Optimization of Ensemble Models for Spam Email Detection
## 2 Related Work
WEKA was used to train and explore the performance of the different classifiers and identify the best-performing one for classifying email spam. The classifiers' performance was evaluated based on the standard evaluation metrics used for machine learning models and the training time. random forest outperformed the other models for all metrics evaluated and achieved a weighted F-measure of 95.50%. naive Bayes also did well in terms of execution time. This study [22] evaluated the performance of five classifiers: logistic regression, decision tree, naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). They used the WEKA tool to train and test the algorithms on the Spambase dataset from the UCI machine-learning repository. The decision tree and KNN algorithms had the best performance, with an accuracy rate of 99% for all metrics. However, KNN took longer to converge than the other algorithms. This study [14] presented a new approach to detecting spam emails that combines the artificial bee colony algorithm with a logistic regression classification model. The proposed method was tested on three publicly available datasets, namely, Enron, CSDMC2010, and TurkishEmail. The model achieved a higher classification accuracy (98.91%) than the other methods considered. The study reported that the proposed method is effective at handling high-dimensional data and performs better than other machine-learning methods, including support vector machine, logistic regression, and naive Bayes, as well as state-of-the-art techniques from previous studies. Towards an accurate detection of spam in mobile message communication, this study [23] developed a machine-learning-based approach for detecting spam messages in mobile device communication. Three classifiers-logistic regression (LR), K-Nearest Neighbor (K-NN), and decision tree (DT)-were applied to the SMS spam collection dataset and evaluated on their ability to classify ham and spam messages. The dataset was split into training and testing sets. The results showed that LR had the highest classification performance, with an accuracy of 99%, and outperformed K-NN and DT with 95% and 98% accuracy, respectively. This study [13] integrated KNN with five bio-inspired algorithms to optimize the spam email detection of the KNN model. The bio-inspired algorithms are Grey wolf optimization, Firefly optimization, Chicken swarm optimization, Grasshopper optimization, and Whale optimization. In the study, alongside the evaluation of the performance of each of the algorithms with KNN, the performance of distance measures such as Manhattan, Euclidean, and Chebychev was measured. The findings revealed that the Whale optimization algorithm integrated with the KNN model is quite promising for most of the evaluation metrics. In order to improve spam detection, this study [12] examines the use of machine-learning techniques on email text data. Six different algorithms (naive Bayes, K-Nearest Neighbors, SVM, logistic regression, decision tree, and random forest) are applied to classify emails as spam or non-spam using natural language processing. These algorithms are trained and tested on a dataset, and the results show that logistic regression and naive Bayes have the highest accuracy rates of up to 99%. The authors propose that the findings of this study could be used to create a more effective spam detection classifier through the combination of different algorithms or filtering methods. This study [4] developed a Convolutional Neural Network (CNN) model for image-based spam email detection. The CNN model was composed of one input layer and two convolution layers with 32 and 64 kernels in the hidden layer. The CNN model was trained and tested on the Dredze and Image Spam Hunter (ISH) dataset. The model achieved an 88% F1 score on the validation set and a 97% F1 score on the dataset's test samples. In this study [24], a method for detecting spam emails was proposed that combines the naive Bayes algorithm with the Markov Random Field.