论文标题
用于多元功能数据的分类集合，并应用于Web调查中的鼠标移动
Classification ensembles for multivariate functional data with application to mouse movements in web surveys
论文作者
论文摘要
我们提出了用于多元功能数据分类的新集合模型，作为半金属弱学习者的组合。我们的模型将当前的半米类型方法从单变量扩展到多变量案例，提出新的半光线来计算函数之间的距离，并考虑使用堆叠的概括方法组合弱学习者的更灵活的选项。我们应用这些合奏模型来确定受访者在调查问题上的困难，以提高调查数据质量。作为难度的预测因素，我们使用受访者与Web调查的互动中的鼠标运动轨迹，在该调查中，操纵了几个问题，以创建两种情况，这些情况不同。
We propose new ensemble models for multivariate functional data classification as combinations of semi-metric-based weak learners. Our models extend current semi-metric-type methods from the univariate to the multivariate case, propose new semi-metrics to compute distances between functions, and consider more flexible options for combining weak learners using stacked generalisation methods. We apply these ensemble models to identify respondents' difficulty with survey questions, with the aim to improve survey data quality. As predictors of difficulty, we use mouse movement trajectories from the respondents' interaction with a web survey, in which several questions were manipulated to create two scenarios with different levels of difficulty.
