论文标题
对立情绪状态分类的机器学习
Machine Learning For Classification Of Antithetical Emotional States
论文作者
论文摘要
通过脑电图信号的情绪分类取得了许多进步。但是，诸如缺乏数据和学习重要特征和模式之类的问题一直是具有计算和预测准确性改进的领域。这项工作分析了基线机器学习分类器在DEAP数据集上的性能以及表格学习方法，该方法提供了最新的可比结果，从而利用了性能提升，这是由于其深度学习架构而无需部署繁重的神经网络。
Emotion Classification through EEG signals has achieved many advancements. However, the problems like lack of data and learning the important features and patterns have always been areas with scope for improvement both computationally and in prediction accuracy. This works analyses the baseline machine learning classifiers' performance on DEAP Dataset along with a tabular learning approach that provided state-of-the-art comparable results leveraging the performance boost due to its deep learning architecture without deploying heavy neural networks.
