1 code implementation • 21 Mar 2024 • Ben Cravens, Andrew Lensen, Paula Maddigan, Bing Xue
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.
no code implementations • 6 Mar 2024 • Paula Maddigan, Andrew Lensen, Bing Xue
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.
no code implementations • 18 Apr 2023 • Peng Zeng, Xiaotian Song, Andrew Lensen, Yuwei Ou, Yanan sun, Mengjie Zhang, Jiancheng Lv
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.
no code implementations • 17 Jan 2023 • Fintan O'Sullivan, Kirita-Rose Escott, Rachael C. Shaw, Andrew Lensen
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.
no code implementations • 15 Aug 2022 • Harry Rodger, Andrew Lensen, Marcin Betkier
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.
no code implementations • 23 Aug 2021 • Andrew Lensen, Bing Xue, Mengjie Zhang
Recently, genetic programming has emerged as a very promising approach to manifold learning by evolving functional mappings from the original space to an embedding.
no code implementations • 2 Feb 2021 • Andrew Lensen
To the best of our knowledge, our proposed approach is the first such symbolic approach with the goal of explicitly discovering relationships between features.
1 code implementation • 27 Jan 2020 • Andrew Lensen, Bing Xue, Mengjie Zhang
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.
no code implementations • 5 Jan 2020 • Andrew Lensen, Mengjie Zhang, Bing Xue
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.
no code implementations • 22 Oct 2019 • Andrew Lensen, Bing Xue, Mengjie Zhang
In this paper, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming.
no code implementations • 8 Feb 2019 • Andrew Lensen, Bing Xue, Mengjie Zhang
Exploratory data analysis is a fundamental aspect of knowledge discovery that aims to find the main characteristics of a dataset.
no code implementations • 2 Feb 2018 • Andrew Lensen, Bing Xue, Mengjie Zhang
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.