Abstract
Generating decision rule sets from observational data is an established branch of machine learning. Although such rules may be well-suited to machine execution, a human being may have problems interpreting them. Making inferences about the dependencies of a number of attributes on each other by looking at the rules is hard, hence the need to summarize and visualize a rule set. In this paper we propose using dependence diagrams as a means of illustrating the amount of influence each attribute has on others. Such information is useful in both causal and non-causal contexts. We provide examples of dependence diagrams using rules extracted from two datasets.
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Karimi, K., Hamilton, H.J. (2008). Using Dependence Diagrams to Summarize Decision Rule Sets. In: Bergler, S. (eds) Advances in Artificial Intelligence. Canadian AI 2008. Lecture Notes in Computer Science(), vol 5032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68825-9_16
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DOI: https://doi.org/10.1007/978-3-540-68825-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68821-1
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