| Full text | |
| Author(s): |
Spadoto, Andre A.
;
Guido, Rodrigo C.
;
Carnevali, Felipe L.
;
Pagnin, Andre F.
;
Falcao, Alexandre X.
;
Papa, Joao P.
;
IEEE
Total Authors: 7
|
| Document type: | Journal article |
| Source: | 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC); v. N/A, p. 4-pg., 2011-01-01. |
| Abstract | |
Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification. (AU) | |
| 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 |