Exploring Open Dimensions in Artificial Immune Systems for High-Dimensional Tasks

High-dimensional (HD) data is relevant to a wide range of fields e.g. remote sensing, network security, medicine and e-commerce. Improved methods are needed to handle such HD data. Artificial Immune Systems (AIS) have shown promise across various classification tasks. However, they are challenged for applications with HD spaces that thus limit their application to such fields. The work herein proposes a novel AIS-based model, the Flexible-Dimension-AIS (FD-AIS), incorporating open dimensions into antibody recognition regions (where antigens can be discovered) so as to address this challenge. Open dimensions enable ABs to extend coverage without being constrained by fixed boundaries, thereby allowing for improved coverage with reduced computational overhead. Key contributions of the model include the introduction of flexible, adaptable recognition regions. Experimental results highlighted the positive effect of unbounded recognition regions in FD-AIS, particularly with respect to efficiency - important when working with high-dimensional data.