| Full text | |
| Author(s): |
da Costa, Kelton A. P.
;
Pereira, Clayton R.
;
Pereira, Luis A. M.
;
Nakamura, Rodrigo M.
;
Papa, Joao Paulo
Total Authors: 5
|
| Document type: | Journal article |
| Source: | JOURNAL OF INFORMATION ASSURANCE AND SECURITY; v. 8, n. 2, p. 10-pg., 2013-01-01. |
| Abstract | |
TIn this paper we deal with the problem of boosting the Optimum-Path Forest (OPF) clustering approach using evolutionary-based optimization techniques. As the OPF classifier performs an exhaustive search to find out the size of sample's neighborhood that allows it to reach the minimum graph cut as a quality measure, we compared several optimization techniques that can obtain close graph cut values to the ones obtained by brute force. Experiments in two public datasets in the context of unsupervised network intrusion detection have showed the evolutionary optimization techniques can find suitable values for the neighborhood faster than the exhaustive search. Additionally, we have showed that it is not necessary to employ many agents for such task, since the neighborhood size is defined by discrete values, with constrain the set of possible solution to a few ones. (AU) | |
| FAPESP's process: | 11/14058-5 - Exploring Sequential Learning Approaches for Optimum-Path Forest |
| Grantee: | Rodrigo Yuji Mizobe Nakamura |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 09/16206-1 - New trends on optimum-path forest-based pattern recognition |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 11/14094-1 - Exploring Multi-labeling Approaches by Optimum-Path Forest |
| Grantee: | Luis Augusto Martins Pereira |
| Support Opportunities: | Scholarships in Brazil - Master |