Extreme Learning machines and Support Vector Machines models for email spam detection

Abstract

Extreme Learning machines (ELM) and Support Vector Machines have become two of the most widely used machine learning techniques for both classification and regression problems of recent. However the comparison of both ELM and SVM for classification and regression problems has often caught the attention of several researchers. In this work, an attempt has been made at investigating how SVM and ELM compared on the unique and important problem of Email spam detection, which is a classification problem. The importance of email in this present age cannot be overemphasized. Hence the need to promptly and accurately detect and isolate unsolicited mails through spam detection system cannot be over emphasized. Empirical results from experiments carried out using very popular dataset indicated that both techniques outperformed the best earlier published techniques on the same popular dataset employed in this study. However, SVM performed better than ELM on comparison scale based on accuracy. But in term of speed of operation, ELM outperformed SVM significantly.