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Table 2 Results on simulated data: experiments were done using increasing number of features.

From: A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays

  

HierNB Classifier

StNB Classifier

Exp

N Feat.

Acc

Brier

Acc

Brier

1

1

0.925 [0.917, 0.933]

0.112

0.874 [0.864, 0.884]

0.184

2

2

0.966 [0.960, 0.971]

0.052

0.921 [0.912, 0.929]

0.118

3

3

0.987 [0.983, 0.990]

0.020

0.946 [0.938, 0.952]

0.082

4

10

0.998 [0.997, 0.999]

0.002

0.985 [0.981, 0.989]

0.023

  1. Exp = number of experiment, N. Feat = Number of features, Acc = Accuracy, Brier = Brier Score. In brackets the 95% confidence intervals for the estimate of the accuracy.
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BMC Bioinformatics

ISSN: 1471-2105

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