Gene Subset Selection for Transfer Learning using Bilevel Particle Swarm Optimization

Hassen Dhrif, Veronica Bolon-Canedo, Stefan Wuchty

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

Abstract

In classification problems that involve multiple sources, data distributions may vary. Therefore, knowledge of dimensions that differ in the source and target data is important to reduce the distance between domains, allowing accurate transfer knowledge. Here, we present a novel method to identify (in)variant genes between source and target datasets and integrate such results to simultaneously reduce the variance between two distributions while optimizing the size and classification error of the selected subset. In particular, we use an evolutionary computation particle swarm optimization algorithm to implement such a bilevel multi-objective programming approach, allowing us to solve a gene subset selection problem.

Original languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
EditorsM. Arif Wani, Feng Luo, Xiaolin Li, Dejing Dou, Francesco Bonchi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1317-1323
Number of pages7
ISBN (Electronic)9781728184708
DOIs
StatePublished - Dec 2020
Event19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 - Virtual, Miami, United States
Duration: Dec 14 2020Dec 17 2020

Publication series

NameProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

Conference

Conference19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
Country/TerritoryUnited States
CityVirtual, Miami
Period12/14/2012/17/20

Keywords

  • Bilevel programming
  • Domain adaptation
  • Feature selection
  • Gene expression data
  • Particle swarm optimization
  • Transfer learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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