  Unsupervised aggregation of independently built univariate predictors is
explored as an alternative regularization approach for noisy, sparse datasets.
Bipartite ranking algorithm Smooth Rank implementing this approach is
introduced. The advantages of this algorithm are demonstrated on two types of
problems. First, Smooth Rank is applied to two-class problems from bio-medical
field, where ranking is often preferable to classification. In comparison
against SVMs with radial and linear kernels, Smooth Rank had the best
performance on 8 out of 12 benchmark benchmarks. The second area of application
is survival analysis, which is reduced here to bipartite ranking in a way which
allows one to use commonly accepted measures of methods performance. In
comparison of Smooth Rank with Cox PH regression and CoxPath methods, Smooth
Rank proved to be the best on 9 out of 10 benchmark datasets.
