Learning High-Order Feature Interactions in Supervised Learning Classifier Systems Using Code Fragments
Loading...
Files
Size: 464.23 KB, File format: Adobe PDF
Date
Authors
Ma, Le
Siddique, Abubakar
Nguyen, Trung
Iqbal, Muhammad
Browne, Will
Supervisor
Item type
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
ACM
Abstract
Learning Classifier Systems (LCSs) are rule-based evolutionary frameworks that combine global search with local learning to produce interpretable predictive models. Supervised LCSs, such as ExSTraCS, have demonstrated strong performance on noisy and high-dimensional datasets; however, their reliance on fixed-length, interval-based rule conditions limits their ability to capture higherorder feature interactions. To address this limitation, we propose Code Fragment-based ExSTraCS (CF-ExSTraCS), a novel supervised LCS framework that integrates genetic programming-based code fragments into the ExSTraCS architecture. Experimental results demonstrate that CF-ExSTraCS substantially outperforms the baseline ExSTraCS system on high-dimensional Boolean tasks and consistently achieves superior or comparable performance across diverse visual feature representations.
Description
Keywords
feature patterns, code fragment, learning classifier systems, rule-based machine learning, feature selection, explainable AI
Source
GECCO '26 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion. July 13-17, 2026. San Jose, Costa Rica. Pages 209 - 212
Publisher's version
Rights statement
Creative Commons Attribution International 4.0
Permanent link
Endorsement
Review
Supplemented By
Referenced By
Creative Commons license
Except where otherwise noted, this item's license is described as Creative Commons Attribution International 4.0