关于使用一组分类器有效预测自闭症谱系障碍治疗
Bhekisipho Twala1, Eamon Molloy2
1Office of the Deputy Vice-Chancellor (Digital Transformation), Tshwane University of Technology, Private Bag x680, Pretoria, 001, South Africa. twalab@tut.ac.za.
Scientific reports
|November 15, 2023
概括
集体学习系统通过优于单个分类器显著改善了自闭症谱系障碍治疗 (ASDT) 预测. 多阶段设计,特别是具有三个核心分类器的设计,显示出ASDT干预的最有前途.
科学领域:
- 机器学习 机器学习
- 计算神经科学是一种神经科学.
- 发展心理学 发展心理学
背景情况:
- 集体学习方法结合多个分类器来提高预测准确性.
- 预测和识别影响自闭症谱系障碍治疗 (ASDT) 的因素对于有效的干预至关重要.
- 机器人系统为客观测量和ASDT的交付提供了潜力.
研究的目的:
- 调查集合学习对预测ASDT结果的有效性.
- 在ASDT预测中比较单个分类器与多个分类器学习系统 (MCLS) 的性能.
- 确定导致ASDT的关键因素.
主要方法:
- 在来自61名自闭症儿童的数据集上评估了5个单一分类器和各种MCLS.
- 利用3000次会议和300小时的机器人增强疗法与标准人类治疗的行为数据.
- 采用决策树作为基准单一分类器,并分析了包装和增强组合架构.
主要成果:
- 在ASDT预测的单个分类器中,决策树显示出更高的准确性.
- MCLS,特别是带有三个核心分类器的合集,实现了更好的ASDT预测性能.
- 用多阶段设计包装和促进合奏学习对于ASDT预测证明是强大的.
结论:
- 集体学习系统,特别是多阶段设计,在ASDT预测中比单个分类器提供了显著的优势.
- 眼神接触和社交互动成为影响ASDT的关键因素.
- 需要进一步开发用于ASDT测量和干预的机器人系统.
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