论文标题
跨主体情绪识别使用稀疏标记的外围生理数据，使用塑造的树团
Cross-Subject Emotion Recognition with Sparsely-Labeled Peripheral Physiological Data Using SHAP-Explained Tree Ensembles
论文作者
论文摘要
尽管最近取得了重大进展，但使用生理数据仍然存在许多情绪识别的挑战。在本文中，我们试图解决两个主要挑战。首先，为了处理稀疏标记的生理数据，我们首先使用信号频谱分析分解了原始生理数据，我们提取了复杂性和能量特征。这样的过程有助于降低噪声并提高特征提取效率。其次，为了通过生理数据提高机器学习模型在情感识别中的解释性，我们提出了光梯度增强机（LightGBM）和Shapley添加说明（SHAP），分别用于情绪预测和模型解释。 The LightGBM model outperformed the eXtreme Gradient Boosting (XGBoost) model on the public Database for Emotion Analysis using Physiological signals (DEAP) with f1-scores of 0.814, 0.823, and 0.860 for binary classification of valence, arousal, and liking, respectively, with cross-subject validation using eight peripheral physiological signals.此外，Shap模型能够识别情绪识别中最重要的特征，并揭示了预测变量与响应变量之间的关系，从其主要效果和相互作用效应方面。因此，所提出的模型的结果不仅使用外围生理数据具有良好的性能，而且还提供了对识别情绪的潜在机制的更多见解。
There are still many challenges of emotion recognition using physiological data despite the substantial progress made recently. In this paper, we attempted to address two major challenges. First, in order to deal with the sparsely-labeled physiological data, we first decomposed the raw physiological data using signal spectrum analysis, based on which we extracted both complexity and energy features. Such a procedure helped reduce noise and improve feature extraction effectiveness. Second, in order to improve the explainability of the machine learning models in emotion recognition with physiological data, we proposed Light Gradient Boosting Machine (LightGBM) and SHapley Additive exPlanations (SHAP) for emotion prediction and model explanation, respectively. The LightGBM model outperformed the eXtreme Gradient Boosting (XGBoost) model on the public Database for Emotion Analysis using Physiological signals (DEAP) with f1-scores of 0.814, 0.823, and 0.860 for binary classification of valence, arousal, and liking, respectively, with cross-subject validation using eight peripheral physiological signals. Furthermore, the SHAP model was able to identify the most important features in emotion recognition, and revealed the relationships between the predictor variables and the response variables in terms of their main effects and interaction effects. Therefore, the results of the proposed model not only had good performance using peripheral physiological data, but also gave more insights into the underlying mechanisms in recognizing emotions.
