Using Genetic Programming to Find Functional Mappings for UMAP Embeddings

Schofield, Finn; Lensen, Andrew

doi:10.1109/cec45853.2021.9504848

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…While visualisation quality was lower in some cases than standard t-SNE, the authors showed that an EMO approach could produce a range of visualisations at different levels of model (tree) complexity. Recent work has further improved the performance of GP in producing such functional mappings [256], [259].…”

Section: A Visualisation By Gpmentioning

confidence: 99%

Explainable Artificial Intelligence by Genetic Programming: A Survey

Mei

Chen

Lensen

et al. 2023

IEEE Trans. Evol. Computat.

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Explainable artificial intelligence has received great interest in the recent decade, due to its importance in critical application domains such as self-driving cars, law and healthcare. Genetic programming is a powerful evolutionary algorithm for machine learning. Compared with other standard machine learning models such as neural networks, the models evolved by GP tend to be more interpretable due to their model structure with symbolic components. However, interpretability has not been explicitly considered in genetic programming until recently, following the surge in popularity of explainable artificial intelligence. This paper provides a comprehensive review of the studies on genetic programming that can potentially improve the model interpretability, both explicitly and implicitly, as a byproduct. We group the existing studies related to explainable artificial intelligence by genetic programming into two categories. The first category considers the intrinsic interpretability, aiming to directly evolve more interpretable (and effective) models by genetic programming. The second category focuses on posthoc interpretability, which uses genetic programming to explain other black-box machine learning models, or explain the models evolved by genetic programming by simpler models such as linear models. This comprehensive survey demonstrates the strong potential of genetic programming for improving the interpretability of machine learning models and balancing the complex trade-off between model accuracy and interpretability.

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Section: A Visualisation By Gpmentioning

confidence: 99%

Explainable Artificial Intelligence by Genetic Programming: A Survey

Mei

Chen

Lensen

et al. 2023

IEEE Trans. Evol. Computat.

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“…The GP-MaL fitness is somewhat ad-hoc: it uses a particular formulation of neighbour preservation. Later work has proposed other fitness functions, such as that used by the state-of-the-art non-mapping UMAP [17] method [22].…”

Section: Evolutionary Computation For Dimensionality Reductionmentioning

confidence: 99%

A Genetic Programming Encoder for Increasing Autoencoder Interpretability

Schofield¹,

Slyfield²,

Lensen³

2023

Lecture Notes in Computer Science

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Autoencoders are powerful models for non-linear dimensionality reduction. However, their neural network structure makes it difficult to interpret how the high dimensional features relate to the lowdimensional embedding, which is an issue in applications where explainability is important. There have been attempts to replace both the neural network components in autoencoders with interpretable genetic programming (GP) models. However, for the purposes of interpretable dimensionality reduction, we observe that replacing only the encoder with GP is sufficient. In this work, we propose the Genetic Programming Encoder for Autoencoding (GPE-AE). GPE-AE uses a multi-tree GP individual as an encoder, while retaining the neural network decoder. We demonstrate that GPE-AE is a competitive non-linear dimensionality reduction technique compared to conventional autoencoders and a GP based method that does not use an autoencoder structure. As visualisation is a common goal for dimensionality reduction, we also evaluate the quality of visualisations produced by our method, and highlight the value of functional mappings by demonstrating insights that can be gained from interpreting the GP encoders.

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“…One approach is GP-tSNE [27], which adapts the classic t-SNE [54] algorithm to use evolved trees to provide an interpretable mapping from the original data points to the embedded points. Similarly, Schofield and Lensen [46] use tree-GP to produce an interpretable mapping for Uniform Manifold Approximation and Projection (UMAP). By producing an explicit mapping function rather than simply the embedded points, we can not only make the process more transparent but also reuse the mapping on new data.…”

Section: Explaining Datamentioning

confidence: 99%

Genetic Programming for Interpretable and Explainable Machine Learning

2023

Genetic and Evolutionary Computation

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Machine learning models are increasingly being used in critical sectors, but their black-box nature has raised concerns about accountability and trust. The field of explainable artificial intelligence (XAI) or explainable machine learning (XML) has emerged in response to the need for human understanding of these models. Evolutionary computing, as a family of powerful optimization and learning tools, has significant potential to contribute to XAI/XML. In this chapter, we provide a brief introduction to XAI/XML and review various techniques in current use for explaining machine learning models. We then focus on how evolutionary computing can be used in XAI/XML, and review some approaches which incorporate EC techniques. We also discuss some open challenges in XAI/XML and opportunities for future research in this field using EC. Our aim is to demonstrate that evolutionary computing is well-suited for addressing current problems in explainability, and to encourage further exploration of these methods to contribute to the development of more transparent, trustworthy and accountable machine learning models.

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Using Genetic Programming to Find Functional Mappings for UMAP Embeddings

Cited by 6 publications

References 14 publications

Explainable Artificial Intelligence by Genetic Programming: A Survey

Explainable Artificial Intelligence by Genetic Programming: A Survey

A Genetic Programming Encoder for Increasing Autoencoder Interpretability

Genetic Programming for Interpretable and Explainable Machine Learning

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