Department of Electrical Electronics and Communication Engineering, GITAM Institute of Technology, GITAM Deemed University, Visakhapatnam, Andhra Pradesh 530045, India.
Dambi Dollo University, Dambi Dollo, Ethiopia.
Comput Intell Neurosci. 2022 Aug 27;2022:4464603. doi: 10.1155/2022/4464603. eCollection 2022.
Autism Spectrum Disorder (ASD) is a complicated collection of neurodevelopmental illnesses characterized by a variety of developmental defects. It is a binary classification system that cannot cope with reality. Furthermore, ASD, data label noise, high dimension, and data distribution imbalance have all hampered the existing classification algorithms. As a result, a new ASD was proposed. This strategy employs label distribution learning (LDL) to deal with label noise and uses support vector regression (SVR) to deal with sample imbalance. The experimental results show that the proposed method balances the effects of majority and minority classes on outcomes. It can effectively deal with imbalanced data in ASD diagnosis, and it can help with ASD diagnosis. This study presents a cost-sensitive approach to correct sample imbalance and uses a support vector regression (SVR)-based method to remove label noise. The label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. This technique outperforms previous methods in terms of classification performance and accuracy, as well as resolving the issue of unbalanced data in ASD diagnosis.
自闭症谱系障碍（ASD）是一种复杂的神经发育障碍，其特征是多种发育缺陷。它是一种不能应对现实的二元分类系统。此外，ASD、数据标签噪声、高维性和数据分布不平衡都阻碍了现有的分类算法。因此，提出了一种新的 ASD。该策略采用标签分布学习（LDL）来处理标签噪声，并使用支持向量回归（SVR）来处理样本不平衡。实验结果表明，所提出的方法平衡了多数类和少数类对结果的影响。它可以有效地处理 ASD 诊断中的不平衡数据，有助于 ASD 的诊断。本研究提出了一种基于代价敏感的方法来纠正样本不平衡，并使用基于支持向量回归（SVR）的方法来消除标签噪声。标签分布学习方法通过将样本映射到特征空间来解决高维特征分类问题，然后对多类 ASD 进行诊断。该技术在分类性能和准确性方面优于以前的方法，解决了 ASD 诊断中数据不平衡的问题。
