An Enhanced Genetic Programming Approach for Detecting Unsolicited Emails

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Trivedi:2013:CSE,

• author = "Shrawan Kumar Trivedi and Shubhamoy Dey",
• title = "An Enhanced Genetic Programming Approach for Detecting Unsolicited Emails",
• booktitle = "16th IEEE International Conference on Computational Science and Engineering (CSE 2013)",
• year = "2013",
• month = "3-5 " # dec,
• pages = "1153--1160",
• address = "Sydney",
• keywords = "genetic algorithms, genetic programming, spam, Enhanced Genetic Programming, SVM, J48, Random Forest, Probabilistic classifiers, Unsolicited Emails, Machine Learning Classifiers, Ensemble, Performance Accuracy, F-Value, False Positive Rate, Sensitivity",
• DOI = "doi:10.1109/CSE.2013.171",
• abstract = "Identification of unsolicited emails (spams) is now a well-recognised research area within text classification. A good email classifier is not only evaluated by performance accuracy but also by the false positive rate. This research presents an Enhanced Genetic Programming (EGP) approach which works by building an ensemble of classifiers for detecting spams. The proposed classifier is tested on the most informative features of two public ally available corpi (Enron and Spam assassin) found using Greedy stepwise search method. Thereafter, the proposed ensemble of classifiers is compared with various Machine Learning Classifiers: Genetic Programming (GP), Bayesian, Naive Bayes (NB), J48, Random forest (RF), and SVM. Results of this study indicate that the proposed classifier (EGP) is the best classifier among those compared in terms of performance accuracy as well as false positive rate.",
• notes = "Also known as \cite{6755352}",

}

Genetic Programming entries for Shrawan Kumar Trivedi Shubhamoy Dey

Citations