Conformal feature-selection wrappers for instance transfer
Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 91:96-113, 2018.
In this paper we propose a new method of conformal feature-selection wrappers for instance transfer (CFSWIT). Given target and source data, the method optimally selects features and source data that are relevant for a classification model. The CFSWIT method is model-independent. It was tested experimentally for several types of classifiers. The experiments show that the CFSWIT method is capable of outperforming standard instance transfer methods.