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Found 12 papers, 2 papers with code
Genetic Programming for Explainable Manifold Learning
Our experimental analysis demonstrates that GP-EMaL is able to match the performance of the existing approach in most cases, while using simpler, smaller, and more interpretable tree structures.
Explaining Genetic Programming Trees using Large Language Models
In this research, we investigate the potential of leveraging eXplainable AI (XAI) and large language models (LLMs) like ChatGPT to improve the interpretability of GP-based non-linear dimensionality reduction.
Differentiable Genetic Programming for High-dimensional Symbolic Regression
With these designs, the proposed DGP method can efficiently search for the GP trees with higher performance, thus being capable of dealing with high-dimensional SR. To demonstrate the effectiveness of DGP, we conducted various experiments against the state of the arts based on both GP and deep neural networks.
Feature-based Image Matching for Identifying Individual Kākā
Applied with a similarity network for clustering, this addresses a weakness of current supervised approaches to identifying individual birds which struggle to handle the introduction of new individuals to the population.
Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand
The judiciary has historically been conservative in its use of Artificial Intelligence, but recent advances in machine learning have prompted scholars to reconsider such use in tasks like sentence prediction.
Genetic Programming for Manifold Learning: Preserving Local Topology
Recently, genetic programming has emerged as a very promising approach to manifold learning by evolving functional mappings from the original space to an embedding.
Mining Feature Relationships in Data
To the best of our knowledge, our proposed approach is the first such symbolic approach with the goal of explicitly discovering relationships between features.
Genetic Programming for Evolving a Front of Interpretable Models for Data Visualisation
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.
Multi-Objective Genetic Programming for Manifold Learning: Balancing Quality and Dimensionality
This method required the dimensionality of the embedding to be known a priori, which makes it hard to use when little is known about a dataset.
Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis
In this paper, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming.
Can Genetic Programming Do Manifold Learning Too?
Exploratory data analysis is a fundamental aspect of knowledge discovery that aims to find the main characteristics of a dataset.
Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming
Recently, feature selection has become an increasingly important area of research due to the surge in high-dimensional datasets in all areas of modern life.
