Spam Classification Using Genetic Algorithm

Atta, Rand Ahmad; Gbashi, Ekhlas K.; Hashem, Soukaena H.

doi:10.34279/0923-009-002-009

Cited by 1 publication

(2 citation statements)

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“…Unsupervised, semi-supervised, and supervised machine learning techniques are the three types used, and in general, supervised learning performs better than the other techniques. Several Machine Learning Algorithms (MLA) can be employed for knowledge identification, including Naive Bayes (NB), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and k-Nearest Neighbor (KNN) [10]- [13].…”

Section: Introductionmentioning

confidence: 99%

“…The two types of feature selection algorithms are filters and wrappers. Gain ratio, information gain, chi-squared, and correlation-based feature selection are a few examples of statistical, information theory-based, or searching methods that can be used to apply filters [13], [14]. Wrappers evaluate and categorise capabilities using a machine learning technique to determine the subset that, for the most part, makes up the dataset.…”

Section: Introductionmentioning

confidence: 99%

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Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm

et al. 2022

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Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam. By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity. SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection. The difficulty with spam classifiers is the abundance of features. The importance of feature selection (FS) comes from its role in directing the feature selection algorithm's search for ways to improve the SDS's classification performance and accuracy. As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training. The suggested system's performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets. Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively. INDEX TERMSSpam detection system (SDS), grasshopper optimization algorithm (GOA), feature selection (FS), multi-objective optimization (MOO), multilayer perceptron (MLP)

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%