Multi-decision-tree classifier in Master Data Management System

A simplified Decision Tree ID3 algorithm was advanced in this paper, and it overcame the existing bias of ID3 algorithm. And then, ADABOOST Algorithm and improved ID3 Algorithm were constituted a multi-decision-tree classifier, and it was applied in Master Data Management System to form the redundant data judgment module which responsibility is judging the redundant data. The result shows that the accuracy of this classifier is better than pure Decision-Tree classifier, and the training duration of this classifier is shorter than original-Decision-Tree-ID3 based ADABOOST classifier. It greatly reduces the manual labor after applying it in Master Data Management System, and saves the consumption of human and material resources.