Ranking and Combining Latent Structured Predictive Scores without Labeled Data

TL;DR

This paper introduces a novel unsupervised ensemble learning model that ranks and combines multiple correlated predictors without labeled data, using correlation-based algorithms, demonstrated through simulations and real-world risk gene discovery.

Contribution

The paper proposes the SUEL model and two correlation-based algorithms for unsupervised predictor ranking and combination, addressing challenges of dependency and lack of labeled data.

Findings

Effective predictor integration without labeled data

Superior performance in simulations and real-world applications

Robustness to predictor correlation

Abstract

Combining multiple predictors obtained from distributed data sources to an accurate meta-learner is promising to achieve enhanced performance in lots of prediction problems. As the accuracy of each predictor is usually unknown, integrating the predictors to achieve better performance is challenging. Conventional ensemble learning methods assess the accuracy of predictors based on extensive labeled data. In practical applications, however, the acquisition of such labeled data can prove to be an arduous task. Furthermore, the predictors under consideration may exhibit high degrees of correlation, particularly when similar data sources or machine learning algorithms were employed during their model training. In response to these challenges, this paper introduces a novel structured unsupervised ensemble learning model (SUEL) to exploit the dependency between a set of predictors with continuous predictive scores, rank the predictors without labeled data and combine them to an ensembled score with weights. Two novel correlation-based decomposition algorithms are further proposed to estimate the SUEL model, constrained quadratic optimization (SUEL.CQO) and matrix-factorization-based (SUEL.MF) approaches. The efficacy of the proposed methods is rigorously assessed through both simulation studies and real-world application of risk genes discovery. The results compellingly demonstrate that the proposed methods can efficiently integrate the dependent predictors to an ensemble model without the need of ground truth data.