Abstract
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 extended notion of likelihood based on uncertain data. The resulting classifier is shown to outperform alternative methods in partially supervised learning tasks.
This research was supported by the Center of Excellence in Econometrics at Chiang Mai University.
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- 1.
Available at http://archive.ics.uci.edu/ml.
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Kanjanatarakul, O., Kuson, S., Denoeux, T. (2018). An Evidential K-Nearest Neighbor Classifier Based on Contextual Discounting and Likelihood Maximization. In: Destercke, S., Denoeux, T., Cuzzolin, F., Martin, A. (eds) Belief Functions: Theory and Applications. BELIEF 2018. Lecture Notes in Computer Science(), vol 11069. Springer, Cham. https://doi.org/10.1007/978-3-319-99383-6_20
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