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Table 7 Variation in classification accuracy as a function of parameters for consensus-GE on gene-expression data

From: Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

Dataset ϕ A c c ( X ̃ G E S ) ϕ A c c ( X ̃ G E U S )
  M = 200 M = 500 M = 1000 M = 200 M = 500 M = 1000
Prostate Tumor 100 100 97.06 76.47 76.47 76.47
Breast Cancer Relapse 57.89 57.89 57.89 57.89 57.89 57.89
Lung Cancer 98.66 98.66 97.99 100 100 90.60
Lymphoma 61.76 97.06 55.88 67.65 67.65 67.65
  1. Classification accuracies for testing cohorts of 4 different binary class gene-expression datasets for X ̃ G E S and X ̃ G E U S , while varying the number of subsets M generated within CreateEmbed.