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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth Inc. (1984)
Brunk, C., Kelly, J., Kohavi, R.: MineSet: An Integrated System for Data Access, Visual Data Mining, and Analytical Data Mining. In: The Third Conference on Knowledge Discovery and Data Mining (KDD 1997), Newport Beach, USA (August 1997)
Karimi, K., Hamilton, H.J.: TimeSleuth: A Tool for Discovering Causal and Temporal Rules. In: 14th IEEE International Conference On Tools with Artificial Intelligence (ICTAI 2002), Washington DC, USA, November 2002, pp. 375–380 (2002)
Karimi, K., Hamilton, H.J.: Distinguishing Causal and Acausal Temporal Relations. In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS (LNAI), vol. 2637, pp. 234–240. Springer, Heidelberg (2003)
Karimi, K., Hamilton, H.J.: Using TimeSleuth for Discovering Temporal/Causal Rules: A Comparison. In: Gedeon, T.D., Fung, L.C.C. (eds.) AI 2003. LNCS (LNAI), vol. 2903, pp. 175–189. Springer, Heidelberg (2003)
Kohavi, R.: The Power of Decision Tables. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 174–189. Springer, Heidelberg (1995)
Michalski, R.S.: A Planar Geometric Model for Representing Multidimensional Discrete Spaces and Multiple-valued Logic Functions, Technical Report UIUCDCS-R-78-897, University of Illinois at Urbana-Champaign, USA (1978)
Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge (2000)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Contents change with time, http://typhoon.bae.lsu.edu/datatabl/current/sugcurrh.html
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-68825-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68821-1
Online ISBN: 978-3-540-68825-9
eBook Packages: Computer ScienceComputer Science (R0)