Name
Abstract | ||
|---|---|---|
In many applications, one has to actively select among a set of expensive observations before making an informed decision. In this paper, we describe a hybrid of a simple artificial intelligence algorithm and a method based on class separability applied to the selection of feature subsets for classication problems. The method allows an expert to discover informative features for separation of normal and attack instances. Experiments performed on the KDD Cup dataset show that explanations provided by the method reveal the nature of attacks. Application of the method for feature selection yields a major improvement of detection accuracy. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/FSKD.2009.451 | FSKD (1) |
Keywords | Field | DocType |
intrusion detection,attack instance,feature subsets,robust observation selection,detection accuracy,classication problem,feature selection yield,expensive observation,class separability,kdd cup dataset show,informative feature,informed decision,artificial intelligent,artificial intelligence,learning artificial intelligence,svm,feature selection,support vector machines,feature extraction,mutual information,accuracy | Feature selection,Pattern recognition,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Mutual information,Class separability,Intrusion detection system,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Cheng | 1 | 0 | 0.34 |
| Yuan Tian | 2 | 0 | 0.34 |
| Yong-Qin Cui | 3 | 0 | 0.34 |
| Jun-Na Zhang | 4 | 0 | 0.34 |