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

arXiv: https://arxiv.org/abs/2205.13380 · HTML: https://mlsanity.com/paper/2205.13380/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 60 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 6 of 6)

- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [A review on distance based time series classification](https://mlsanity.com/paper/1806.04509/) (2018)
- [Functional Regression](https://mlsanity.com/paper/1406.4068/) (2014)
- [Elastic analysis of irregularly or sparsely sampled curves](https://mlsanity.com/paper/2104.11039/) (2021)
- [Benchmarking time series classification -- Functional data vs machine learning approaches](https://mlsanity.com/paper/1911.07511/) (2019)
- [Nonparametric Regression and Classification with Functional, Categorical, and Mixed Covariates](https://mlsanity.com/paper/2111.03115/) (2021)

## Abstract (arXiv)

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.