Publications

@article{janez-martino_classification_2020,

title = {Classification of spam emails through hierarchical clustering and supervised learning},

author = {Francisco Jánez-Martino and Eduardo Fidalgo and Santiago González and Javier Velasco-Mata},

url = {https://arxiv.org/abs/2005.08773},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {arXiv preprint arXiv:2005.08773},

abstract = {This work introduces SPEMC-11K, the first multi-class spam email dataset, categorizing spam into Health and Technology, Personal Scams, and Sexual Content. Using TF-IDF and BOW with Naïve Bayes, Decision Trees, and SVM, the best accuracy (95.39% F1-score) is achieved with TF-IDF and SVM, while TF-IDF and NB offer the fastest classification (2.13ms per email).},

keywords = {Cybersecurity, machine learning, Spam Classification, Text Processing, TF-IDF & BOW},

pubstate = {published},

tppubtype = {article}

}