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.< >