使用经典机器学习对情感模型进行个性化:可行性研究
Ali Kargarandehkordi1, Matti Kaisti2, Peter Washington1
1Information and Computer Sciences Department, University of Hawai'i at Manoa, Honolulu, HI 96822, USA.
概括
针对情绪识别的个性化机器学习 (ML) 模型优于通用模型. 这项研究表明,个性化的模型获得更高的F1分数,但成功取决于个人数据的变化.
科学领域:
- 情感计算是一种情感计算.
- 机器学习 机器学习
- 人与计算机的互动.
背景情况:
- 传统的情感识别模型经常使用一种适合所有人的方法.
- 情感计算中的个性化可以导致更准确,更细微的情感检测.
研究的目的:
- 调查个性化机器学习模型在情感识别方面的有效性.
- 使用生理和视频数据,将个性化模型与通用模型进行比较.
主要方法:
- 训练了来自Emognition数据集的51个特征的个性化k-最近邻居,随机森林和密集的神经网络.
- 确保每个受试者的训练和测试数据之间的时间分离.
- 将个性化模型的表现 (F1分数) 与所有学科受过训练的通用模型进行比较.
主要成果:
- 与一般化模型相比,个性化的模型获得了更高的平均F1得分 (例如,随机森林的92.66%) (例如,91.78%).
- 在10个受试者中,个性化模型在7个受试者中表现优于一般化模型.
- 主要组件分析 (PCA) 表明,低个体内数据变化阻碍了个性化模型的性能.
结论:
- 个性化的机器学习模型显示了提高情绪识别准确性的巨大潜力.
- 个性化模型的有效性取决于在主体内有足够的数据变化.
- 需要进一步的研究来解决实施个性化情感计算的挑战.
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