Name
Abstract | ||
|---|---|---|
Spam is considered an invasion of privacy. Its changeable structures and variability raise the need for new spam classification techniques. The present study proposes using Bayesian Additive Regression Trees (BART) for spam classification and evaluates its performance against other classification methods, including Logistic Regression, Support Vector Machines, Classification and Regression Trees, Neural Networks, Random Forests, and Naive Bayes. BART in its original form is not designed for such problems, hence we modify BART and make it applicable to classification problems. We evaluate the classifiers using three spam datasets; Ling-Spam, PU1, and Spambase to determine the predictive accuracy and the false positive rate. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ARES.2008.136 | Barcelona |
Keywords | Field | DocType |
new spam classification technique,neural networks,spam datasets,bayesian additive regression trees,classification method,enhanced email privacy,logistic regression,bayesian additive regression trees-based,regression trees,classification problem,spam detection,naive bayes,spam classification,classification,support vector machines,random forest,bayesian methods,data privacy,regression tree,false positive rate,accuracy,logistics,support vector machine,privacy,random forests,regression analysis,svm,neural network,spam | False positive rate,Data mining,Naive Bayes classifier,Regression analysis,Computer science,Support vector machine,Artificial intelligence,Artificial neural network,Information privacy,Random forest,Machine learning,Bayesian probability | Conference |
ISBN | Citations | PageRank |
978-0-7695-3102-1 | 9 | 0.57 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saeed Abu-Nimeh | 1 | 303 | 16.70 |
| Dario Nappa | 2 | 120 | 5.95 |
| Xinlei Wang | 3 | 228 | 16.47 |
| Suku Nair | 4 | 140 | 12.00 |