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Table 3 Statistics about the comparative performance of the base k-NN classifiers and their label similarity-incorporated versions on small classes (size ≤ 30), measured in terms of the number of classes for which AUC scores are improved by the latter over the former, and the average and maximum improvement in AUC scores over all classes.

From: Incorporating functional inter-relationships into protein function prediction algorithms

Dataset

# Small classes

# Classes improved

Average improvement over all small classes

Maximum improvement

Mnaimneh

47

27

0.0358 (6.24%)

0.1882 (39.92%)

Rosetta

48

21

0.0225 (3.82%)

0.2091 (38.66%)

Krogan

40

14

0.0129 (1.89%)

0.1982 (31.82%)

Combined

48

28

0.0197 (2.72%)

0.1129 (20.39%)