An Enhanced Genetic Programming Approach for Detecting Unsolicited Emails

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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