In the past decades, the rapid growth of computer and database technologies has led to the rapid growth of large-scale medical datasets. On the other, medical applications with high dimensional datasets that require high speed and accuracy are rapidly increasing. One of the dimensionality reduction approaches is feature selection that can increase the accuracy of the disease diagnosis and reduce its computational complexity. In this paper, a novel PSO-based multi objective feature selection method is proposed. The proposed method consists of three main phases. In the first phase, the original features are showed as a graph representation model. In the next phase, feature centralities for all nodes in the graph are calculated, and finally, in the third phase, an improved PSO-based search process is utilized to final feature selection. The results on five medical datasets indicate that the proposed method improves previous related methods in terms of efficiency and effectiveness.

在过去的几十年中，计算机和数据库技术的快速增长导致了大规模医疗数据集的快速增长。另一方面，需要高速度和准确性的高维数据集的医学应用正在迅速增加。降维方法之一是特征选择，它可以提高疾病诊断的准确性并降低其计算复杂度。在本文中，提出了一种新的基于 PSO 的多目标特征选择方法。所提出的方法由三个主要阶段组成。在第一阶段，原始特征显示为图形表示模型。在下一阶段，计算图中所有节点的特征中心度，最后，在第三阶段，利用改进的基于 PSO 的搜索过程进行最终的特征选择。