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.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v91-zhou18a, title = {Conformal feature-selection wrappers for instance transfer}, author = {Zhou, Shuang and Smirnov, Evgueni and Schoenmakers, Gijs and Peeters, Ralf and Jiang, Tao}, booktitle = {Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications}, pages = {96--113}, year = {2018}, editor = {Gammerman, Alex and Vovk, Vladimir and Luo, Zhiyuan and Smirnov, Evgueni and Peeters, Ralf}, volume = {91}, series = {Proceedings of Machine Learning Research}, month = {11--13 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v91/zhou18a/zhou18a.pdf}, url = {https://proceedings.mlr.press/v91/zhou18a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Conformal feature-selection wrappers for instance transfer %A Shuang Zhou %A Evgueni Smirnov %A Gijs Schoenmakers %A Ralf Peeters %A Tao Jiang %B Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2018 %E Alex Gammerman %E Vladimir Vovk %E Zhiyuan Luo %E Evgueni Smirnov %E Ralf Peeters %F pmlr-v91-zhou18a %I PMLR %P 96--113 %U https://proceedings.mlr.press/v91/zhou18a.html %V 91 %X 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.
APA
Zhou, S., Smirnov, E., Schoenmakers, G., Peeters, R. & Jiang, T.. (2018). Conformal feature-selection wrappers for instance transfer. Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 91:96-113 Available from https://proceedings.mlr.press/v91/zhou18a.html.

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