A Novel Classification Method and its application in Flow Forecast

摘要

In this paper, a new adaptive classification fusion method was proposed based on the Dempster-Shafer’s theory of evidence and fuzzy Kohonen neural network. The new method integrated ideas from unsupervised neural network model and using neighborhood information in the framework of the Dempster-Shafer theory of evidence. This approach mainly consists in considering each neighbor of a pattern to be classified as an item of evidence supporting certain hypotheses concerning the class membership of that pattern. This evidence is represented by basic probability assignment (BPA’s) and pooled using the Dempster’s rule of combination. Experiments demonstrate the excellent performance of this method as compared with existing neural network techniques