The classical decision tree (DT) learning approach us constructing DT knowledge bases is usually not considered if there exist some irrelevant and redundant attributes in the problem domain. Since the essential attributes are uncertain in manufacturing systems, how to select important manufacturing attributes to improve the generalization ability of knowledge bases and avoid overfitting training data in DT-based learning is a crucial research issue for the adaptive scheduling problem domain. In this study, we will first develop an attribute selection algorithm based on the weights of artificial neural networks (ANNs) to identify the importance of system attributes. Next, we will use the C4.5 DT learning algorithm to learn the whole set of training examples with importaut attributes in order to enhance knowledge representation. This hybrid ANN/DT approach is called an attribute selection DT (ASDT) based learning adaptive scheduling system. The results from the case study show that the use of an attribute selection algorithm to build scheduling knowledge bases delivers better generalization ability than in the absence of use attribute selection procedure in terms of the size of DTs under various performance criteria. Consistent conclusions are drawn from the resulting prediction accuracy of unseen data. The resulting prediction accuracy of unseen data also reveals that scheduling knowledge bases by the proposed attribute selection approach to constructing DTs can avoid overfitting the training data compared with the classical DT learning approach.
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