Name
Abstract | ||
|---|---|---|
In this paper, we propose a feature selection method to speed up online SVM based spam filter. Online SVM gives state-of-the-art classification performance on online spam filtering on large benchmark data sets. However, its computational cost is very expensive for large-scale applications. Feature Selection is a crucial step to online SVM classification. We use a feature selection method based on Bayesian reasoning in this paper, and it based on n-gram feature extraction. The Feature Selection method can reduce feature vector dimension and improve the filter performance a little. It can greatly reduce the computational cost of Online SVMs based spam filter. Experimental results show that the feature selection method outperforms pure online SVM for large-scale spam filtering. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IALP.2010.37 | IALP |
Keywords | Field | DocType |
benchmark data sets,online svm based spam filtering,feature selection,pure online svm,n-gram feature extraction,computational cost,feature selection method,inference mechanisms,bayesian reasoning,information filtering,filter performance,support vector machine (svm),feature vector dimension,unsolicited e-mail,spam filtering,large-scale spam,online svm,online spam,support vector machines,gram feature extraction,svm classification,bayesian methods,feature extraction,support vector machine,feature vector,filtering,cognition | Feature vector,Bayesian inference,Pattern recognition,Feature selection,Computer science,Support vector machine,Filter (signal processing),Feature extraction,Artificial intelligence,Machine learning,Speedup,Bayesian probability | Conference |
ISBN | Citations | PageRank |
978-1-4244-9063-9 | 0 | 0.34 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuewu Shen | 1 | 0 | 0.34 |
| Guang-Lu Sun | 2 | 58 | 16.03 |
| Haoliang Qi | 3 | 130 | 26.56 |
| Xiaoning He | 4 | 18 | 5.88 |