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Discovering inter-attribute relationships

Abstract
It is important to discover relationships between attributes being used to predict a class attribute in supervised learning situations for two reasons. First, any such relationship will be potentially interesting to the provider of a dataset in its own right. Second, it would simplify a learning algorithm’s search space, and the related irrelevant feature and subset selection problem, if the relationships were removed from datasets ahead of learning. An algorithm to discover such relationships is presented in this paper. The algorithm is described and a surprising number of inter-attribute relationships are discovered in datasets from the University of California at Irvine (UCI) repository.
Type
Working Paper
Type of thesis
Series
Computer Science Working Papers
Citation
Holmes, G. (1997). Discovering inter-attribute relationships. (Working paper 97/13). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
Date
1997-04
Publisher
Degree
Supervisors
Rights