Stable Feature Selection for Gene Expression using Enhanced Binary Particle Swarm Optimization

Hassen Dhrif, Stefan Wuchty

Abstract

Feature subset selection (FSS) is an intractable optimization problem in high-dimensional gene expression datasets, leading to an explosion of local minima. While binary variants of particle swarm optimization (BPSO) have been applied to solve the FSS problem, increasing dimensionality of the feature space pose additional challenges to these techniques imparing their ability to select most relevant feature subsets in the massive presence of uninformative features. Most FSS optimization techniques focus on maximizing classification performance while minimizing subset size but usually fail to account for solution stability or feature relevance in their optimization process. In particular, stability in FSS is interpreted differently compared to PSO. Although a large volume of published studies on each stability issue separately exists, wrapper models that tackle both stability problems at the same time are still missing. Specifically, we introduce a novel ap-praoch COMBPSO (COMBinatorial PSO) that features a novel fitness function, integrating feature relevance and solution stability measures with classification performance and subset size as well as PSO adaptations to enhance the algorithm’s convergence abilities. Applying our approach to real disease-specific gene expression data, we found that COMBPSO has similar classification performance compared to BPSO, but provides reliable classification with considerably smaller and more stable gene subsets.

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Paper Citation


in Harvard Style

Dhrif H. and Wuchty S. (2020). Stable Feature Selection for Gene Expression using Enhanced Binary Particle Swarm Optimization.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 437-444. DOI: 10.5220/0008919004370444


in Bibtex Style

@conference{icaart20,
author={Hassen Dhrif and Stefan Wuchty},
title={Stable Feature Selection for Gene Expression using Enhanced Binary Particle Swarm Optimization},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={437-444},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008919004370444},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Stable Feature Selection for Gene Expression using Enhanced Binary Particle Swarm Optimization
SN - 978-989-758-395-7
AU - Dhrif H.
AU - Wuchty S.
PY - 2020
SP - 437
EP - 444
DO - 10.5220/0008919004370444