A Classification Method for E-mail Spam Using a Hybrid Approach for Feature Selection Optimization

Document Type : Original Research Articles

Authors
Zeinab Hassani 1
vahid Hajihashemi 2
Keivan Borna 3
Iman Sahraei Dehmajnoonie 4
1 Department of computer science, Kosar University of Bojnord, Iran.
2 Student Member, IEEE
3 Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, IRAN
4 Science and Research Branch, Islamic Azad University, kerman, Iran
10.22059/jsciences.2020.288729.1007444
Abstract
Spam is an unwanted email that is harmful to communications around the world. Spam leads to a growing problem in a personal email, so it would be essential to detect it. Machine learning is very useful to solve this problem as it shows good results in order to learn all the requisite patterns for classification due to its adaptive existence. Nonetheless, in spam detection, there are a large number of features to attend as they play an essential role in detection efficiency. In this article, we're working on a feature selection method to e-mail spam. This approach is considered a hybrid of optimization algorithms and classifiers in machine learning. Binary Whale Optimization (BWO) and Binary Grey Wolf Optimization (BGWO) algorithms are used for feature selection and K-Nearest Neighbor (KNN) and Fuzzy K-Nearest Neighbor (FKNN) algorithms are applied as the classifiers in this research. The proposed method is tested on the "SPAMBASE" datasets from UCI Machine learning Repesotries and the experimental results revealed the highest accuracy of 97.61% on this dataset. The obtained results indicateed that the proposed method is suitable and capable to provide excellent performance in comparison with other methods.
Keywords
Spam Mails
Whale Optimization Algorithm
Grey Wolf Optimization Algorithm
Fuzzy K-Nearest Neighbor algorithm (FKNN)
Feature Selection
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Journal of Sciences, Islamic Republic of Iran
Volume 31, Issue 2
Spring 2020
Pages 165-173