Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm

Abstract

Spam and emails have always been intrinsically linked since the creation of the Advanced Research Projects Agency Network, otherwise known as (ARPANET). The latter witnessed, on May 3rd, 1978, the first known spam email to date. Today, spam emails negatively affect the users' productivity and private lives. A significant number of approaches emerged in the past two decades that deal with the spam detection problem, with limited success. Therefore, the current paper presents an intelligent and automated solution to spam email detection using a logistic regression model trained by a teaching-learning-based optimization algorithm. The proposed solution has been tested on two benchmark spam email datasets (CSDMC2010 and TurkishEmail), and evaluated against seven other contending cutting-edge metaheuristics utilized in the same experimental setup. The simulation outcomes without a doubt indicate the superior level of accuracy achieved by the proposed solution.

Cross-platform spam messages classification based on the multiple machine learning algorithms

“…Logistic Regression is a very commonly used machine learning algorithm with very low time complexity [6]. For its capability in data analysis, a lot of software companies have a Logistic regression model as their products' innate feature [7]. Random forest is also a very popular machine-learning algorithm.…”