Journal ArticleDOI
Spam classification: a comparative analysis of different boosted decision tree approaches
TLDR
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.Abstract:
Purpose
Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification accuracy but also by sensitivity (correctly classified legitimate emails) and specificity (correctly classified unsolicited emails) towards the accurate classification, captured by both false positive and false negative rates. This paper aims to present a comparative study between various decision tree classifiers (such as AD tree, decision stump and REP tree) with/without different boosting algorithms (bagging, boosting with re-sample and AdaBoost).
Design/methodology/approach
Artificial intelligence and text mining approaches have been incorporated in this study. Each decision tree classifier in this study is tested on informative words/features selected from the two publically available data sets (SpamAssassin and LingSpam) using a greedy step-wise feature search method.
Findings
Outcomes of this study show that without boosting, the REP tree provides high performance accuracy with the AD tree ranking as the second-best performer. Decision stump is found to be the under-performing classifier of this study. However, with boosting, the combination of REP tree and AdaBoost compares favourably with other classification models. If the metrics false positive rate and performance accuracy are taken together, AD tree and REP tree with AdaBoost were both found to carry out an effective classification task. Greedy stepwise has proven its worth in this study by selecting a subset of valuable features to identify the correct class of emails.
Research limitations/implications
This research is focussed on the classification of those email spams that are written in the English language only. The proposed models work with content (words/features) of email data that is mostly found in the body of the mail. Image spam has not been included in this study. Other messages such as short message service or multi-media messaging service were not included in this study.
Practical implications
In this research, 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. This classifier may be tested for different applications and may provide new insights for developers and researchers.
Originality/value
A comparison of decision tree classifiers with/without ensemble has been presented for spam classification.read more
Citations
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Journal ArticleDOI
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade
TL;DR: This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade.
Journal ArticleDOI
A study on credit scoring modeling with different feature selection and machine learning approaches
TL;DR: A combination of random forest and Chi-Square is found good, among other combinations, with respect to good performance accuracy, F-measure and low false positive and false negative rates.
Journal ArticleDOI
Hybrid Water Cycle Optimization Algorithm With Simulated Annealing for Spam E-mail Detection
TL;DR: The aim of this research is to improve the accuracy of feature selection by applying hybrid Water Cycle and Simulated Annealing to optimize results and to evaluate the proposed Spam Detection.
Journal ArticleDOI
An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factors
TL;DR: An empirical study to validate the performance of common learning algorithms under three different environments for email classification and indicates that LibSVM and SMO‐SVM can achieve better performance than other selected algorithms.
Journal ArticleDOI
Spam emails in academia: issues and costs
TL;DR: This paper addresses issues associated with spam emails, and a conservative estimate of the external costs of spam from publishers and journals amounts to US$ 1.1 billion per year, and when all spam emails are included in the calculation, the cost rises to approximately US$ 2.6 billion per year.
References
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Statistical Comparisons of Classifiers over Multiple Data Sets
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.
Proceedings Article
Experiments with a new boosting algorithm
Yoav Freund,Robert E. Schapire +1 more
TL;DR: This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
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Journal ArticleDOI
The Strength of Weak Learnability
TL;DR: In this paper, a method is described for converting a weak learning algorithm into one that achieves arbitrarily high accuracy, and it is shown that these two notions of learnability are equivalent.