Uncertain reasoning in an ID3 machine learning framework

Quinlan´s ID3 is a symbolic machine learning algorithm which uses training examples as input and constructs a decision tree as output. One problem with the standard decision tree approach to machine learning is that uncertain data, either in training and/or testing, often produces poor classification accuracies. The UR-ID3 algorithm described combines uncertain reasoning with the rule set produced by ID3 to create a machine learning algorithm which is robust in the presence of uncertain training and testing data. Experimental results are presented which compare the new algorithm´s performance with that of ID3 and backpropagation neural networks