Tree models for difference and change detection in a complex environment

TL;DR

This paper introduces differential tree models that detect distributional differences across multiple datasets, especially in high-dimensional environments, demonstrated through an arson case study.

Contribution

The paper presents a novel nonparametric tree-based methodology for change detection across multiple datasets in complex, high-dimensional settings.

Findings

Effective detection of distributional differences in complex data

Identification of unusual event clusters in a real-world case

Applicable to high-dimensional change detection scenarios

Abstract

A new family of tree models is proposed, which we call "differential trees." A differential tree model is constructed from multiple data sets and aims to detect distributional differences between them. The new methodology differs from the existing difference and change detection techniques in its nonparametric nature, model construction from multiple data sets, and applicability to high-dimensional data. Through a detailed study of an arson case in New Zealand, where an individual is known to have been laying vegetation fires within a certain time period, we illustrate how these models can help detect changes in the frequencies of event occurrences and uncover unusual clusters of events in a complex environment.