发表机构
Deakin University; University of New South Wales（迪肯大学; 新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大学生动机缺乏和快感缺失症状预测中的类别不平衡问题，提出一种利用预测模型生成名义变量的新颖过采样方法，并在GPS数据集上验证其显著优于现有方法。
AI 中文摘要
大学生经历常见心理健康问题（如抑郁症）的比例异常高，这些问题可能损害学习、社交功能和整体幸福感。在此背景下，动机缺乏（即失去动机驱动力）和快感缺失（即兴趣或愉悦感减退）的症状尤其令人衰弱，却常常未被发现。开发新方法来识别具有显著动机缺乏和快感缺失症状的学生，可以促成更早和更有针对性的干预。机器学习（ML）方法已越来越多地用于根据症状严重程度对个体进行分类。然而，这些ML模型常常遭受类别不平衡问题，其中大多数案例属于低症状组，而相对少数属于高症状组。这种不平衡可能降低模型准确性并使预测产生偏差。为解决此问题，研究通常采用流行的过采样策略SMOTE。然而，SMOTE有一个显著局限：它可能为名义变量生成无效值。在本文中，我们引入了一种新颖且有效的过采样方法来解决这一缺陷。我们的方法利用预测模型来生成名义变量，而非对其进行插值。我们在从大学生收集的大规模GPS位置数据集上验证了我们的方法，并证明其在预测动机缺乏和快感缺失症状升高方面显著优于现有的过采样方法。
英文摘要
University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well-being. Within this context, symptoms of amotivation (i.e. loss of motivational drive) and anhedonia (i.e. diminished interest or pleasure) are particularly debilitating, yet they frequently go undetected. Developing new approaches to identify students with prominent amotivation and anhedonia could enable earlier and more targeted intervention. Machine learning (ML) methods have increasingly been used to classify individuals according to symptom severity. However, these ML models often suffer from class imbalance, where the majority of cases fall in the low-symptom group and relatively few in the high-symptom group. This imbalance can reduce model accuracy and bias predictions. To address this, studies commonly employ the popular oversampling strategy SMOTE. However, SMOTE has a notable limitation: it may generate invalid values for nominal variables. In this paper, we introduce a novel and effective oversampling method that addresses this shortcoming. Our approach leverages a predictive model to generate nominal variables, rather than interpolating them. We validate our method on a large-scale GPS location dataset collected from university students and demonstrate that it is significantly better than existing oversampling approaches in predicting elevated symptoms of amotivation and anhedonia.
