Ensemble Risk Modeling Method for Robust Learning on Scarce Data

TL;DR

This paper introduces Smooth Rank, a new ensemble bipartite ranking algorithm designed for robust medical risk modeling with scarce, high-dimensional, and censored data, outperforming standard methods especially when data is limited.

Contribution

The paper presents a novel bipartite ranking algorithm, Smooth Rank, that effectively handles scarce, high-dimensional, and censored data in medical risk modeling, with demonstrated superior performance.

Findings

Smooth Rank outperforms standard methods on real datasets.

It maintains robustness and avoids overfitting with limited data.

The method is validated through experiments on artificial and real data.

Abstract

In medical risk modeling, typical data are "scarce": they have relatively small number of training instances (N), censoring, and high dimensionality (M). We show that the problem may be effectively simplified by reducing it to bipartite ranking, and introduce new bipartite ranking algorithm, Smooth Rank, for robust learning on scarce data. The algorithm is based on ensemble learning with unsupervised aggregation of predictors. The advantage of our approach is confirmed in comparison with two "gold standard" risk modeling methods on 10 real life survival analysis datasets, where the new approach has the best results on all but two datasets with the largest ratio N/M. For systematic study of the effects of data scarcity on modeling by all three methods, we conducted two types of computational experiments: on real life data with randomly drawn training sets of different sizes, and on artificial data with increasing number of features. Both experiments demonstrated that Smooth Rank has critical advantage over the popular methods on the scarce data; it does not suffer from overfitting where other methods do.