Logistic ensembles for random spherical linear oracles

Leif E. Peterson, Matthew A. Coleman

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


A random spherical linear oracle (RSLO) ensemble classifier for DNA microarray gene expression data is proposed. The oracle assigns different training(testing) samples to 2 sub-classifiers of the same type using hyperplane splits in order to increase the diversity of voting results since errors are not shared across sub-classifiers. Eleven classifiers were evaluated for performance as the base classifier including k nearest neighbor (kNN), naïve Bayes classifier (NBC), linear discriminant analysis (LDA), learning vector quantization (LVQ1), polytomous logistic regression (PLOG), artificial neural networks (ANN), constricted particle swarm optimization (CPSO), kernel regression (KREG), radial basis function networks (RBFN), gradient descent support vector machines (SVMGD), and least squares support vector machines (SVMLS). Logistic ensembles (PLOG) resulted in the best performance when used as a base classifier for RSLO. Random hyperplane splits used in RSLO resulted in degeneration of performance at the greatest levels of CV-fold and iteration number when compared with hyperplane splits in principal direction linear oracle (PDLO), which increased with increasing CV-fold and iteration number.

Original languageEnglish (US)
Title of host publicationProceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007
Number of pages6
StatePublished - Dec 1 2007
Event6th International Conference on Machine Learning and Applications, ICMLA 2007 - Cincinnati, OH, United States
Duration: Dec 13 2007Dec 15 2007


Other6th International Conference on Machine Learning and Applications, ICMLA 2007
Country/TerritoryUnited States
CityCincinnati, OH

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Control and Systems Engineering


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