Classification ensembles for multivariate functional data with application to mouse movements in web surveys

TL;DR

This paper introduces novel ensemble models for classifying multivariate functional data, specifically applied to mouse movement trajectories in web surveys to assess question difficulty and improve data quality.

Contribution

It extends semi-metric-based classification methods from univariate to multivariate data, introduces new semi-metrics, and employs stacked generalisation for flexible ensemble modeling.

Findings

Effective identification of survey question difficulty using mouse movement data.

Improved classification accuracy with the proposed ensemble models.

Demonstrated applicability to real-world survey data.

Abstract

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.