Skip to main content
padlock icon - secure page this page is secure

An enhanced knowledge representation for decision-tree based learning adaptive scheduling

Buy Article:

$61.00 + tax (Refund Policy)

The classical decision tree (DT) learning approach to 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 important 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 the 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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Publication date: January 1, 2003

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more