Journal ArticleDOI
Interaction between feature subset selection techniques and machine learning classifiers for detecting unsolicited emails
TLDR
Results show that, Greedy Stepwise Search method is a good method for feature subset selection for spam email detection and among the Machine Learning Classifiers, Support Vector Machine has been found to be the best classifier both in terms of accuracy and False Positive rate.Abstract:
Detection of the spam emails within a set of email files has become challenging task for researchers. Identification of an effective classifier is based not only on high accuracy of detection but also on low false alarm rates, and the need to use as few features as possible. In view of these challenges, this research examines the effects of using features selected by four feature subset selection methods (i.e. Genetic, Greedy Stepwise, Best First, and Rank Search) on popular Machine Learning Classifiers like Bayesian, Naive Bayes, Support Vector Machine, Genetic Algorithm, J48 and Random Forest. Tests were performed on three different publicly available spam email datasets: "Enron", "SpamAssassin" and "LingSpam". Results show that, Greedy Stepwise Search method is a good method for feature subset selection for spam email detection. Among the Machine Learning Classifiers, Support Vector Machine has been found to be the best classifier both in terms of accuracy and False Positive rate. However, results of Random Forest were very close to that of Support Vector Machine. The Genetic classifier was identified as a weak classifier.read more
Citations
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Proceedings ArticleDOI
A study of machine learning classifiers for spam detection
TL;DR: SVM is the best classifier to be used because it has the high accuracy and the low false positive rate and training time of SVM to build the model is high, but as the results on other parameters are positive, the time does not pose such an issue.
Proceedings ArticleDOI
A Comparative Study of Various Supervised Feature Selection Methods for Spam Classification
TL;DR: Results of this study shows that RF is the excellent feature selection technique amongst other in terms of classification accuracy and false positive rate whereas DF and X2 were not so effective methods.
Proceedings ArticleDOI
A Combining Classifiers Approach for Detecting Email Spams
TL;DR: Results show the best results of novel combining classifier approach in compression with individual classifiers compared in terms of good performance accuracy and low false positives.
Journal ArticleDOI
Spam classification: a comparative analysis of different boosted decision tree approaches
TL;DR: A boosted decision tree approach has been proposed and used to classify email spam and ham files; this is found to be a highly effective approach in comparison with other state-of-the-art modes used in other studies.
Proceedings ArticleDOI
Using Adaboost and Stochastic gradient descent (SGD) Algorithms with R and Orange Software for Filtering E-mail Spam
TL;DR: The experimental results showed that the algorithms Adaboost and stochastic gradient descent (SGD) provided true positive value and 98.1% respectively and false positive rates of 0.0% and 1.9% respectively put them among the best choices of spam filtering methods.
References
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