# A topological data analysis based classification method for multiple measurements

## TL;DR

This paper introduces a topological data analysis-based classifier for repeated measurements that outperforms traditional models in accuracy and provides insights into data subsets, with applications in biological sciences.

## Contribution

The paper presents a novel TDA-based classification method for repeated measurements, demonstrating improved accuracy and interpretability over existing models.

## Key findings

- Achieved 80% accuracy with 30 data points, improved to 90% with 400 points on tree species data.
- Reached 96.8% accuracy on point process data with 100 examples per class.
- Outperformed alternative models in both case studies.

## Abstract

Machine learning models for repeated measurements are limited. Using topological data analysis (TDA), we present a classifier for repeated measurements which samples from the data space and builds a network graph based on the data topology. When applying this to two case studies, accuracy exceeds alternative models with additional benefits such as reporting data subsets with high purity along with feature values. For 300 examples of 3 tree species, the accuracy reached 80% after 30 datapoints, which was improved to 90% after increased sampling to 400 datapoints. Using data from 100 examples of each of 6 point processes, the classifier achieved 96.8% accuracy. In both datasets, the TDA classifier outperformed an alternative model. This algorithm and software can be beneficial for repeated measurement data common in biological sciences, as both an accurate classifier and a feature selection tool.