55 citations · 141 across the 9 of their papers we have counts for
6 papers · 1 filter
Differentiable Genetic Programming for High-dimensional Symbolic Regression
Peng Zeng, Xiaotian Song, Andrew Lensen +4
Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable mac…
Genetic Programming for Evolving a Front of Interpretable Models for Data Visualisation
Andrew Lensen, Bing Xue, Mengjie Zhang
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…
Multi-Objective Genetic Programming for Manifold Learning: Balancing Quality and Dimensionality
Andrew Lensen, Mengjie Zhang, Bing Xue
Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of…
Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis
Andrew Lensen, Bing Xue, Mengjie Zhang
Clustering is a difficult and widely-studied data mining task, with many varieties of clustering algorithms proposed in the literature. Nearly all algorithms use a similarity measu…
Can Genetic Programming Do Manifold Learning Too?
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. Dimensionality reduction, such as manifold learnin…
Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming
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. A plethora of feature s…