Self-correcting ensemble using a latent consensus model

Ensemble is a widely used technique to improve the predictive performance of a learning method by using several competing expert systems. In this study, we propose a new ensemble combination scheme using a latent consensus function that relates each predictor to the other. The proposed method is designed to adapt and self-correct weights even when a number of expert systems malfunction and become corrupted. To compare the performance of the proposed method with existing methods, experiments are performed on simulated data with corrupted outputs as well as on real-world data sets. Results show that the proposed method is effective and it improves the predictive performance even when a number of individual classifiers are malfunctioning.