Parallel Multidimensional Uncertain Data Evidence Theory Decision Tree

Abstract

Evidence theory decision tree is an efficient classification technique can be used in uncertain data mining field. But it can’t deal with large training sets of millions of samples which are common in this field. This paper develops parallel algorithm for evidence theory decision tree on the multidimensional cube structure. Example shows this algorithm can treat with very large multidimensional uncertain data training set and shows good parallel performance.