Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 113, Issue -, Pages 287-302Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2019.07.009
Keywords
Belief functions; Dempster-Shafer theory; Classification; Machine learning; Soft labels; Uncertain data
Categories
Funding
- Agence Nationale de la Recherche, Laboratory of Excellence MS2T [ANR-11-IDEX0004-02]
- Centre of Excellence in Econometrics, Research Administration Centre at Chiang Mai University
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The evidential K nearest neighbor classifier is based on discounting evidence from learning instances in a neighborhood of the pattern to be classified. To adapt the method to partially supervised data, we propose to replace the classical discounting operation by contextual discounting, a more complex operation based on as many discount rates as classes. The parameters of the method are tuned by maximizing the evidential likelihood, an extension of the likelihood function based on uncertain data. The resulting classifier is shown to outperform alternative methods in partially supervised learning tasks. (C) 2019 Elsevier Inc. All rights reserved.
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