Feature Selection based on Improved Differential Evolution (DE) Algorithm for E-mail Classification

Spam e-mail has become a pervasive nuisance in today's digital world, posing significant challenges to efficient communication and information dissemination. Dealing with huge amounts of data with irrelevant and redundant features, which leads to high dimension. Nowadays, with the growth of using the internet, finding the secure E-mail classification system for cloud computing is a very important topic. Additionally, determining the best algorithm for choosing a subset of features has a big impact on how well automatic email classification works, making it one of the major issues. Among these is the Differential Evolution (DE) algorithm, which is computationally costly because of the slow convergence rate and evolutionary process. To address these issues, this study offers an intelligent scheme called Opposition Differential Evolution (ODE), which combines the Opposition Based Learning (OBL) and DE algorithms for effective automated feature subset selection. Its effectiveness is assessed using the support vector machine (SVM) to present a strong performance when evaluating the e-mail spam classification rate. Moreover, the OBL is used to accelerate and increase the convergence rate of traditional DE. To determine which features, contribute most to the reliability of the email spam classification, the subset features based on ODE that was used as feature subset selection are used.To assess the effectiveness of the proposed scheme, extensive experiments are conducted on spambase” and “spamassassin” benchmark email datasets, comprising a diverse collection of spam and non-spam emails. The results demonstrate that the Opposition Differential Evolution (ODE) algorithm yields superior performance compared to traditional machine learning and evolutionary techniques, displaying its robustness and efficiency in identifying spam emails accurately. The ODE algorithm effectively handles high-dimensional feature spaces, enhancing the model's discriminatory power while maintaining computational efficiency. Compared to the suggested ODE-SVM technique, which yields a result of 96.79 percent, the full-feature accuracy result was 93.55 percent. Additionally, empirical results demonstrate that our scheme may efficiently increase the number of features needed to improve the accuracy of the email spam classification.