School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
J Biomed Inform. 2018 Aug;84:164-170. doi: 10.1016/j.jbi.2018.07.011. Epub 2018 Jul 17.
Attention Deficit Hyperactive Disorder (ADHD) is one of the most common diseases in school aged children. In this paper, we consider using fMRI data with classification techniques to aid the diagnosis of ADHD and propose a bi-objective ADHD classification scheme based on L-norm support vector machine (SVM). In our classification model, two objectives, namely, the margin of separation and the empirical error are considered at the same time. Then the normal boundary intersection (NBI) method of Das and Dennis is used to solve the bi-objective optimization problem. A representative nondominated set which reflects the entire trade-off information between the two objectives is obtained. Each representative nondominated point in the set corresponds to an efficient classifier. Finally a decision maker can choose a final efficient classifier from the set according to the performance of each classifier. Our scheme avoids the trial and error process for regularization hyper-parameter selection. Experimental results show that our bi-objective optimization classification scheme for ADHD diagnosis performs considerably better than some traditional classification methods.
注意缺陷多动障碍（ADHD）是学龄儿童中最常见的疾病之一。在本文中，我们考虑使用 fMRI 数据和分类技术来辅助 ADHD 的诊断，并提出了一种基于 L-范数支持向量机（SVM）的双目标 ADHD 分类方案。在我们的分类模型中，同时考虑了两个目标，即分离边缘和经验误差。然后使用 Das 和 Dennis 的正常边界交叉（NBI）方法来解决双目标优化问题。得到了一个反映两个目标之间所有权衡信息的代表性非支配集。集中的每个代表性非支配点对应一个有效的分类器。最后，决策者可以根据每个分类器的性能从集中选择一个最终的有效分类器。我们的方案避免了正则化超参数选择的反复试验过程。实验结果表明，我们的 ADHD 诊断双目标优化分类方案的性能明显优于一些传统分类方法。
