Improved classification accuracy by feature extraction using genetic algorithms

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

Original languageEnglish (US)
Pages (from-to)1402-1412
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5032 III
DOIs
StatePublished - Sep 15 2003
EventMedical Imaging 2003: Image Processing - San Diego, CA, United States
Duration: Feb 17 2003Feb 20 2003

Keywords

  • Classification
  • Feature extraction
  • Genetic algorithm

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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