Decision tree algorithm based on average Euclidean distance

Traditionally, the algorithm of ID3 takes the information gain as a standard of expanding attributes. During the process of selection of expanded attributes, attributes with more values are usually preferred to be selected. To solve such problem, a kind of AED algorithm based on average Euclidean distance in decision tree is proposed in this paper. The algorithm uses the average Euclidean distance as heuristic information. The experiment results show that the improved AED algorithm can avoid the variety bias of ID3 algorithm, and has no worse classification precision and less time cost than ID3.