Introduction

AI techniques are highly accurate in identifying spam emails. They can handle large amounts of data, making them well-suited for dealing with the large volumes of emails sent and received daily. Additionally, AI techniques can adapt and improve over time, making them more efficient at identifying new types of spam. Furthermore, by using AI techniques, it is possible to create solid literacies responding to real-time contemporaneous affairs automatically; upgraded versions are to be released regularly as speculations need attention followed by precisely established trustworthiness providing powerful fine-tuning events analogous visual analog gestures carrying the message every aspects life needed to deal impressively with gains resulting in the totality of complete email classification systems.

Problem Domain

Email classification has become a major challenge for modern information management due to the ever-increasing volume of emails linked with digitalization and rapid advances in technology. One preprocessing step that can be accomplished using machine learning is spam detection, which consists of filtering out unwanted emails and ensuring only relevant messages are visible to users. Supervised learning AI techniques are among the most common methods applied to this task due to their capability to discern meaningful concepts from large datasets.

Literature review

Artificial Intelligence (AI) has been widely adopted in recent years for the task of email spam classification. This literature review explores the various AI techniques used for this purpose, their advantages and challenges, and the evaluation metrics used to assess their performance. The study will delve into supervised learning algorithms such as Naive Bayes, Support Vector Machines (SVMs), Random Forest, Gradient Boosting, and unsupervised learning techniques like clustering and anomaly detection. The literature review will also discuss the issues related to imbalanced datasets and the challenges in adapting to new types of spam.

Overview of AI techniques used for email spam classification

Supervised learning algorithms such as Naive Bayes, Support Vector Machines (SVMs), Random Forest, and Gradient Boosting have been commonly used for email spam classification (RAZA, Jayasinghe and Muslam, 2021; Cota and Zinca, 2022; Singh et al., 2022). These algorithms are trained on labelled data and use the patterns in the labeled data to classify new emails as spam or non-spam. In the case of Naive Bayes, it is a probabilistic algorithm that makes assumptions about probability distributions based on past data. SVMs, on the other hand, are robust against overfitting due to their use of decision planes instead of straight lines, which provides good generalization performances even with small datasets (Cota and Zinca, 2022). Random Forest is an ensemble method that combines predictive models into larger decision trees instead of relying only on single instances based on previously trained models. Gradient Boosting uses multiple weak learners chosen randomly from different subsets.

In addition, unsupervised learning techniques such as clustering and anomaly detection have also been used for email spam classification (Awad, 2011). These methods do not rely on labeled data but instead identify data patterns to classify emails as spam or non-spam. Clustering algorithms such as K-means and DBSCAN can be used to group emails based on their characteristics (RAZA, Jayasinghe and Muslam, 2021), and anomaly detection algorithms such as one-class SVM and Random Forest can identify emails that differ from the norm.

Advantages of using AI for email spam classification

AI techniques are highly accurate in identifying spam emails. They can handle large amounts of data, making them well-suited for dealing with the large volumes of emails sent and received daily. Additionally, AI techniques can adapt and improve over time, making them more efficient at identifying new types of spam. Furthermore, by using AI techniques, it is possible to create solid literacies responding to real-time affairs automatically (RAZA, Jayasinghe and Muslam, 2021); upgraded versions are to be released regularly as speculations need attention followed by precisely established trustworthiness providing powerful fine-tuning events analogous visual analog gestures carrying the message every aspects life needed to deal impressively with gains resulting in the totality of complete email classification systems.

Challenges in applying AI for email spam classification

One of the main challenges in applying AI for email spam classification is dealing with imbalanced datasets. Spam emails are often a tiny minority of the total emails, making it difficult for AI algorithms to learn patterns in the data (Mahmoud Jazzar et al., 2021). Additionally, new types of spam are constantly being developed, making it difficult for AI algorithms to keep up (Raja et al., 2022). Furthermore, in the case of unsupervised techniques, the data labeling process can be time-consuming and requires much effort to obtain high-quality labeled data. Finally, AI algorithms require large amounts of labeled data to train, which can be difficult and time-consuming to obtain.

Conclusion

In conclusion, the literature review has provided an overview of the various AI techniques used for email spam classification, including supervised and unsupervised learning methods. These methods accurately identify spam emails and can handle large amounts of data. However, the main challenges in applying AI for email spam classification include dealing with imbalanced datasets, adapting to new types of spam, and the need for large amounts of labeled data to train the algorithms. The performance of AI techniques for email spam classification is typically evaluated using metrics such as accuracy, precision, recall, and F1-score. The review has also highlighted the need for ongoing research to address these challenges and improve the efficiency of these techniques in the long run.

Comparion on ROC Curves

References

Awad, W.A. (2011). Machine Learning Methods for Spam E-Mail Classification. International Journal of Computer Science and Information Technology, 3 (1), 173–184. Available from https://doi.org/10.5121/ijcsit.2011.3112 [Accessed 9 November 2022].

Cota, R.P. and Zinca, D. (2022). Comparative Results of Spam Email Detection Using Machine Learning Algorithms. 2022 14th International Conference on Communications (COMM). June 2022. 1–5. Available from https://doi.org/10.1109/COMM54429.2022.9817305.

Mahmoud Jazzar et al. (2021). Evaluation of Machine Learning Techniques for Email Spam Classification. International Journal of Education and Management Engineering, 11 (4), 35–42. Available from https://doi.org/10.5815/ijeme.2021.04.04 [Accessed 14 January 2023].

Raja, P.V. et al. (2022). Email Spam Classification Using Machine Learning Algorithms. 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS). February 2022. 343–348. Available from https://doi.org/10.1109/ICAIS53314.2022.9743033.

RAZA, M., Jayasinghe, N.D. and Muslam, M.M.A. (2021). A Comprehensive Review on Email Spam Classification using Machine Learning Algorithms. 2021 International Conference on Information Networking (ICOIN). January 2021. 327–332. Available from https://doi.org/10.1109/ICOIN50884.2021.9334020.

Singh, U. et al. (2022). Spam Email Assessment Using Machine Learning and Data Mining Approach. 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT). July 2022. 350–357. Available from https://doi.org/10.1109/CCiCT56684.2022.00070.

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