A study of boosted evolutionary classifiers for detecting spam. Issue 4 (1st November 2019)

• Record Type: Journal Article
• Authors: Trivedi, Shrawan Kumar; Dey, Shubhamoy
• Is Part Of: Global knowledge, memory and communication. Volume 69:Issue 4/5(2020)
• Page Start: 269
• Page End: 287
• Publication Date: 2019-11-01
• Subjects: Genetic algorithms -- Genetic programming -- Support vector machine -- Bayesian classifier -- Naive bayes -- Classification accuracy -- F-value -- False positive rate -- Greedy stepwise search -- AdaBoost; Information science -- Periodicals; Electronic information resources -- Periodicals; Libraries -- Periodicals; Library science -- Periodicals; 020.5
• DOI: 10.1108/GKMC-05-2019-0060
• Languages: English
• ISSNs: 2514-9342

Abstract

Abstract : Purpose: Email is a rapid and cheapest medium of sharing information, whereas unsolicited email (spam) is constant trouble in the email communication. The rapid growth of the spam creates a necessity to build a reliable and robust spam classifier. This paper aims to presents a study of evolutionary classifiers (genetic algorithm [GA] and genetic programming [GP]) without/with the help of an ensemble of classifiers method. In this research, the classifiers ensemble has been developed with adaptive boosting technique. Design/methodology/approach: Text mining methods are applied for classifying spam emails and legitimate emails. Two data sets (Enron and SpamAssassin) are taken to test the concerned classifiers. Initially, pre-processing is performed to extract the features/words from email files. Informative feature subset is selected from greedy stepwise feature subset search method. With the help of informative features, a comparative study is performed initially within the evolutionary classifiers and then with other popular machine learning classifiers (Bayesian, naive Bayes and support vector machine). Findings: This study reveals the fact that evolutionary algorithms are promising in classification and prediction applications where genetic programing with adaptive boosting is turned out not only an accurate classifier but also a sensitive classifier. Results show that initially GA performs better than GP but after an ensemble of classifiers (a large number of iterations), GP overshoots GA with significantly higher accuracy. Amongst all classifiers, boosted GP turns out to be not only good regarding classification accuracy but also low false positive (FP) rates, which is considered to be the important criteria in email spam classification. Also, greedy stepwise feature search is found to be an effective method for feature selection in this application domain. Research limitations/implications: The research implication of this research consists of the reduction in cost incurred because of spam/unsolicited bulk email. Email is a fundamental necessity to share information within a number of units of the organizations to be competitive with the business rivals. In addition, it is continually a hurdle for internet service providers to provide the best emailing services to their customers. Although, the organizations and the internet service providers are continuously adopting novel spam filtering approaches to reduce the number of unwanted emails, the desired effect could not be significantly seen because of the cost of installation, customizable ability and the threat of misclassification of important emails. This research deals with all the issues and challenges faced by internet service providers and organizations. Practical implications: In this research, the proposed models have not only provided excellent performance accuracy, sensitivity with low FP rate, customizable capability but also worked on reducing the cost of spam. The same models may be used for other applications of text mining also such as sentiment analysis, blog mining, news mining or other text mining research. Originality/value: A comparison between GP and GAs has been shown with/without ensemble in spam classification application domain.