Artículos de revistas
A comparison about evolutionary algorithms for optimum-path forest clustering optimization
Fecha
2013Registro en:
Journal of Information Assurance and Security, v. 8, n. 2, p. 76-85, 2013.
1554-1010
3369681396058151
8448107303335081
9039182932747194
5228991166855582
9083697774870852
Autor
Universidade Estadual Paulista (Unesp)
Institución
Resumen
In 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.