Spadoto André A, Guido Rodrigo C, Carnevali Felipe L, Pagnin André F, Falcão Alexandre X, Papa João P
Institute of Physicsat São Carlos, University of São Paulo, São Carlos, Brazil.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7857-60. doi: 10.1109/IEMBS.2011.6091936.
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.
帕金森病（PD）的自动识别在文献中的多项研究中一直受到积极关注。在本文中，我们通过应用基于进化的技术来处理这个问题，以便找到能够最大化最优路径森林（OPF）分类器准确性的特征子集。选择这个分类器的原因在于其训练阶段速度快，因为每个要优化的可能解决方案都以OPF准确性为指导。我们还展示了比最近在PD自动识别背景下获得的其他结果有所改进的结果。
