Bipartite ranking algorithm for classification and survival analysis

TL;DR

This paper introduces Smooth Rank, a bipartite ranking algorithm that aggregates univariate predictors for improved performance in biomedical classification and survival analysis, outperforming traditional methods in benchmarks.

Contribution

The paper presents Smooth Rank, a novel bipartite ranking algorithm that effectively handles noisy, sparse data and demonstrates superior performance over SVMs and Cox models.

Findings

Smooth Rank outperforms SVMs on biomedical classification benchmarks.

Smooth Rank surpasses Cox models in survival analysis datasets.

The method effectively aggregates univariate predictors for improved accuracy.

Abstract

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.