Boosting Trees for Anti-Spam Email Filtering

By Xavier Carreras, Lluís Màrquez i Villodre. arXiv.org, 2001

430 citations recorded by the source corpus.

This paper describes a set of comparative experiments for the problem of automatically filtering unwanted electronic mail messages. Several variants of the AdaBoost algorithm with confidence-rated predictions [Schapire & Singer, 99] have been applied, which differ in the complexity of the base learners considered. Two main conclusions can be drawn from our experiments: a) The boosting-based methods clearly outperform the baseline learning algorithms (Naive Bayes and Induction of Decision Trees) on the PU1 corpus, achieving very high levels of the F1 measure; b) Increasing the complexity of the base learners allows to obtain better ``high-precision'' classifiers, which is a very important issue when misclassification costs are considered.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/92efd2833b37938bad69aef33e70757445376bd5

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Citations in the corpus

A survey of learning-based techniques of email spam filtering (2008, methodology): “However, Näıve Bayes has already been shown to be outperformed by many other methods (see for example [13, 102, 16]), so now a more accurate baseline method is needed, for example Support Vector Machines, as done by Sasaki and Shinnou [81].”
An evaluation of statistical spam filtering techniques (2004, methodology): “…Sahami et al. 1998; Schneider 2003], RIPPER rule induction algorithm [Cohen 1996], support vector machines [Drucker et al. 1999; Kolcz and Alspector 2001], memory-based learning [Androutsopoulos et al. 2000], AdaBoost [Carreras and M´ arquez 2001], and maximum entropy model [Zhang and Yao 2003].”
On Attacking Statistical Spam Filters (2004, methodology): “One of the widely used methods, Bayesian classication, attempts to calculate the probability that a message is spam based upon previous feature frequencies in spam and legitimate e-mail [9, 8, 1, 4]. Other notable learning algorithms applied to spam ltering include boosting [ 2 ] and support vector ”
A review of machine learning approaches to Spam filtering (2009, methodology): “Carreras and Marquez (2001) used an AdaBoost (Hastie et al., 2001) variant, with decision trees as the base classifiers, in experiments with the PU1 corpus, 10-fold cross-validation and binary features.”
Spam Filtering with Naive Bayes - Which Naive Bayes? (2006, methodology): “Although several machine learning algorithms have been employed in anti-spam e-mail filtering, including algorithms that are considered top-performers in text classification, like Boosting and Support Vector Machines (see, for example, [4, 6, 10, 16]), Naive Bayes (nb) classifiers currently appear t”
Spam Detection Using Feature Selection and Parameters Optimization (2010, neutral): “B.3) in detail.”
Overview of BioCreative II gene mention recognition (2008, methodology): “A boosted decision tree algorithm [47,48] was applied to this dataset (holding out 25 sentences at a time, as mentioned above) to learn to classify candidates as positive or negative.”
Ensemble application of convolutional and recurrent neural networks for multi-label text categorization (2017, methodology): “, information retrieval [1], affective computing [2], sentiment analysis [3], email spam detection [4], multimodal content analysis [5], etc.”
Natural language processing: state of the art, current trends and challenges (2017, methodology): “…Rennie.,2000) [5, 109, 115],Memory based Learning (Sakkiset al.,2000b) [117], Support vector machines (Druker et al., 1999) [36], Decision Trees (Carreras and Marquez, 2001) [19], Maximum Entropy Model (Berger et al. 1996) [14], Hash Forest and a rule encoding method (T. Xia, 2020) [153],…”