Ranking and Combining Latent Structured Predictive Scores without Labeled Data.

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 unsupervise

SUBMITTER: Afshar S 

PROVIDER: S-EPMC12345621 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature