  AUC (Area under the ROC curve) is an important performance measure for
applications where the data is highly imbalanced. Learning to maximize AUC
performance is thus an important research problem. Using a max-margin based
surrogate loss function, AUC optimization problem can be approximated as a
pairwise rankSVM learning problem. Batch learning methods for solving the
kernelized version of this problem suffer from scalability and may not result
in sparse classifiers. Recent years have witnessed an increased interest in the
development of online or single-pass online learning algorithms that design a
classifier by maximizing the AUC performance. The AUC performance of nonlinear
classifiers, designed using online methods, is not comparable with that of
nonlinear classifiers designed using batch learning algorithms on many
real-world datasets. Motivated by these observations, we design a scalable
algorithm for maximizing AUC performance by greedily adding the required number
of basis functions into the classifier model. The resulting sparse classifiers
perform faster inference. Our experimental results show that the level of
sparsity achievable can be order of magnitude smaller than the Kernel RankSVM
model without affecting the AUC performance much.
