Ensembles based on conformal instance transfer
Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 105:23-42, 2019.
In this paper we propose a new ensemble method based on conformal instance transfer. The method combines feature selection and source-instance selection to avoid negative transfer in a model-independent way. It was tested experimentally for different types of classifiers on several benchmark data sets. The experiment results demonstrate that the new method is capable of outperforming significantly standard instance transfer methods.