Notice of RetractionEnhanced active learning in developing highly interpretable decision support system

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Developing highly interpretable commonly presents significant challenges to decision support system. In previous research work, partial information had provided poor result in the problem of learning classifiers. The behavior of some learning algorithm may only be explored by uncertainty analyses. We propose a novel information extraction by utilizing fuzzy measure in active learning to focus on the most informative instances. By integrating an expert knowledge as weight to the existing datasets, we overcome the uncertainty and appropriately assign partial datasets to the nearest clusters for classification. By choosing appropriate weights for pre labeled data, the nearest neighbor classifier consistently improves on the original classifier.