Conformal feature-selection wrappers for instance transfer


Shuang Zhou, Evgueni Smirnov, Gijs Schoenmakers, Ralf Peeters, Tao Jiang ;
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.

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