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EEkNN: k-Nearest Neighbor Classifier with an Evidential Editing Procedure for Training Samples
2019
Electronics
The k-nearest neighbor (kNN) rule is one of the most popular classification algorithms applied in many fields because it is very simple to understand and easy to design. However, one of the major problems encountered in using the kNN rule is that all of the training samples are considered equally important in the assignment of the class label to the query pattern. In this paper, an evidential editing version of the kNN rule is developed within the framework of belief function theory. The
doi:10.3390/electronics8050592
fatcat:f2ce5oepprehbnzpu2p6wiwsie