# Genetic Programming for Evolving a Front of Interpretable Models for Data Visualisation

arXiv: https://arxiv.org/abs/2001.09578 · HTML: https://mlsanity.com/paper/2001.09578/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [Explainable Benchmarking for Iterative Optimization Heuristics](https://mlsanity.com/paper/2401.17842/) (2024)
- [Model Learning with Personalized Interpretability Estimation (ML-PIE)](https://mlsanity.com/paper/2104.06060/) (2021)
- [On genetic programming representations and fitness functions for interpretable dimensionality reduction](https://mlsanity.com/paper/2203.00528/) (2022)
- [Genetic Programming for Manifold Learning: Preserving Local Topology](https://mlsanity.com/paper/2108.09914/) (2021)
- [Evolutionary approaches to explainable machine learning](https://mlsanity.com/paper/2306.14786/) (2023)
- [Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems](https://mlsanity.com/paper/2406.07811/) (2024)
- [Mining Feature Relationships in Data](https://mlsanity.com/paper/2102.01355/) (2021)
- [Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence](https://mlsanity.com/paper/2309.14385/) (2023)
- [Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers for Better Biocomposite Materials](https://mlsanity.com/paper/2404.07213/) (2024)

## Abstract (arXiv)

Data visualisation is a key tool in data mining for understanding big datasets. Many visualisation methods have been proposed, including the well-regarded state-of-the-art method t-Distributed Stochastic Neighbour Embedding. However, the most powerful visualisation methods have a significant limitation: the manner in which they create their visualisation from the original features of the dataset is completely opaque. Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualisation methods which use understandable models. In this work, we propose a genetic programming approach named GPtSNE for evolving interpretable mappings from a dataset to highquality visualisations. A multi-objective approach is designed that produces a variety of visualisations in a single run which give different trade-offs between visual quality and model complexity. Testing against baseline methods on a variety of datasets shows the clear potential of GP-tSNE to allow deeper insight into data than that provided by existing visualisation methods. We further highlight the benefits of a multi-objective approach through an in-depth analysis of a candidate front, which shows how multiple models can