Stable feature selection for gene expression using enhanced binary particle swarm optimization

Hassen Dhrif, Stefan Wuchty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 appraoch 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.

Original languageEnglish (US)
Title of host publicationICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
EditorsAna Rocha, Luc Steels, Jaap van den Herik
PublisherSciTePress
Pages437-444
Number of pages8
ISBN (Electronic)9789897583957
StatePublished - 2020
Event12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta
Duration: Feb 22 2020Feb 24 2020

Publication series

NameICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
Volume2

Conference

Conference12th International Conference on Agents and Artificial Intelligence, ICAART 2020
CountryMalta
CityValletta
Period2/22/202/24/20

Keywords

  • Evolutionary Computation
  • Feature Selection
  • Gene Discovery
  • Particle Swarm Optimization
  • Scalability
  • Stability

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Fingerprint Dive into the research topics of 'Stable feature selection for gene expression using enhanced binary particle swarm optimization'. Together they form a unique fingerprint.

Cite this