Name
Abstract | ||
|---|---|---|
A boosting-based cascade for automatic decomposition of multiclass learning problems into several binary classification problems is presented. The proposed cascade structure uses a boosted classifier at each level and use a filtering process to reduce the problem size at each level. The method has been used for detecting malicious traffic patterns using a benchmark intrusion detection dataset. A comparison of the approach with four boosting-based multiclass learning algorithms is also provided on this dataset. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889931 | Neural Networks |
Keywords | Field | DocType |
learning (artificial intelligence),pattern classification,security of data,CBC,automatic decomposition,benchmark intrusion detection dataset,binary classification problems,boosting-based multiclass learning algorithms,cascade of boosted classifiers,filtering process,malicious traffic patterns,multiclass learning problems | Pattern recognition,Computer science,Artificial intelligence,Cascade,Intrusion detection system,Machine learning,Multiclass classification | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mubasher Baig | 1 | 0 | 0.34 |
| El-Sayed M. El-Alfy | 2 | 10 | 3.47 |
| Mian Awais | 3 | 59 | 11.53 |