Inductive learning from preclassified training examples: an empirical study

Many real-world decision-making problems fall into the general category of classification. Algorithms for constructing knowledge by inductive inference from example have been widely used for some decades. Although these learning algorithms frequently address the same problem of learning from preclassified examples and much previous work in inductive learning has focused on the algorithms' predictive accuracy, little attention has been paid to the effect of data factors on the performance of a learning system. An experiment was conducted using five learning algorithms on two data sets to investigate how the change in labeling the class attribute can alter the behavior of learning algorithms. The results show that different preclassification rules applied on the training examples can affect either the classification accuracy or classification structure.