In this paper, an innovative method for estimating mass functions using Kohonen’s Self Organizing Map is proposed. Our approach allows a smart mass belief assignment, not only for simple hypotheses, but also for disjunctions and conjunctions of hypotheses. This new method is of interest for solving estimation mass functions problems where a large quantity of multi-variate data is available. Indeed, the use of Kohonen map that allows to approximate the feature space dimension into a projected 2D space (so called map) simplifies the process of assigning mass functions. Experimentation on a benchmark database shows that our approach gives similar or better results than other methods presented in the literature so far, with an ability to handle large amount of data.
CITATION STYLE
Hammami, I., Dezert, J., Mercier, G., & Hamouda, A. (2014). On the estimation of mass functions using self organizing maps. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8764, 275–283. https://doi.org/10.1007/978-3-319-11191-9_30
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