Assessment of machine-learning tool meta-classifiers combined with balanced voting.
Machine-learning tools were combined using balanced voting to generate meta-classifiers. The best 6 individual classifiers were combined, and with iterative cycles of classifier removal 5, 4, 3 and 2 machine-learning tool meta-classifiers were tested. Survival separation between assigned ABC and GCB classes for the data sets GSE32918, and GSE10846 divided into CHOP and R-CHOP components was used for assessment. The classifiers were ordered by their average rank across the data sets; with rank determined by the p-value of the ABC/GCB separation. The Classifier Identity, Hazard Ratio (vs ABC as baseline), 95% confidence interval of the Hazard Ratio, and the resulting p-value for survival separation are shown.