Logistic ensembles of Random Spherical Linear Oracles for microarray classification

Leif E. Peterson, Matthew A. Coleman

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Random Spherical Linear Oracles (RSLO) for DNA microarray gene expression data are proposed for classifier fusion. RSLO employs random hyperplane splits of samples in the principal component score space based on the first three principal components (X, Y,Z) of the input feature set. Hyperplane splits are used to assign training(testing) samples to separate logistic regression mini-classifiers, which increases the diversity of voting results since errors are not shared across mini-classifiers. We recommend use of RSLO with 3-4 10-fold CV and re-partitioning samples randomly every ten iterations prior to each 10-fold CV. This equates to a total of 30-40 iterations.

Original languageEnglish (US)
Pages (from-to)382-397
Number of pages16
JournalInternational Journal of Data Mining and Bioinformatics
Issue number4
StatePublished - 2009


  • Ensemble classifier fusion
  • Hyperplanes
  • Microarrays
  • PCs
  • Principal components
  • Principal directions
  • Random linear oracle
  • Random spherical linear oracles
  • RSLO

ASJC Scopus subject areas

  • Library and Information Sciences
  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)


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