@inproceedings{145f2340d1f34fd69dc1e63c8e8f9d56,
title = "Evidential editing K-nearest neighbor classifier",
abstract = "One of the difficulties that arises when using the K-nearest neighbor rule is that each of the labeled training samples is given equal importance in deciding the class of the query pattern to be classified, regardless of their typicality. In this paper, the theory of belief functions is introduced into the K-nearest neighbor rule to develop an evidential editing version of this algorithm. An evidential editing procedure is proposed to reassign the original training samples with new labels represented by an evidential membership structure. With the introduction of the evidential editing procedure, the uncertainty of noisy patterns or samples in overlapping regions can be well characterized. After the evidential editing, a classification procedure is developed to handle the more general situation in which the edited training samples are assigned dependent evidential labels. Two experiments based on synthetic and real data sets were carried out to show the effectiveness of the proposed method.",
keywords = "Data classification, Evidential editing, K-nearest neighbor, Theory of belief functions",
author = "Lianmeng Jiao and Thierry Denoeux and Quan Pan",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2015 ; Conference date: 15-07-2015 Through 17-07-2015",
year = "2015",
doi = "10.1007/978-3-319-20807-7_42",
language = "英语",
isbn = "9783319208060",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "461--471",
editor = "S{\'e}bastien Destercke and Thierry Denoeux",
booktitle = "Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 13th European Conference, ECSQARU 2015, Proceedings",
}