Combinatorial Methods for Feature Selection

Project Description: This research explores feature selection through combinatorial optimization techniques, particularly using domination and packing in graphs and networks. By modeling feature selection as a graph-theoretic problem, we leverage structures such as independent dominating sets to identify minimal representative features and packing formulations to ensure both diversity and redundancy reduction. Our approach aims to develop efficient algorithms that balance accuracy and interpretability in machine learning models. We also investigate parameterized complexity aspects and specialized heuristics for large-scale data. Applications include bioinformatics, social network analysis, and high-dimensional data classification.

PI: Faisal N. Abu-Khzam

Partners: Joseph Barr and Peter Shaw.

Publications:

F. N. Abu-Khzam, J. R. Barr, M. R. Benabid and P. Shaw. Feature Selection via Weighted Independent Domination, in proceedings of the 2024 Conference on AI, Science, Engineering, and Technology (AIxSET): 2024, 179-184.