Abstract
Most of the methods that generate decision trees for a specific problem use examples of data instances in the decision tree generation process. This chapter proposes a method called “RBDT-1” - rule based decision tree - for learning a decision tree from a set of decision rules that cover the data instances rather than from the data instances themselves. RBDT-1 method uses a set of declarative rules as an input for generating a decision tree. The method’s goal is to create on-demand a short and accurate decision tree from a stable or dynamically changing set of rules. We conduct a comparative study of RBDT-1 with existing decision tree methods based on different problems. The outcome of the study shows that in terms of tree complexity (number of nodes and leaves in the decision tree) RBDT-1 compares favorably to AQDT-1, AQDT-2 which are methods that create decision trees from rules. RBDT-1 compares favorably also to ID3 which is a famous method that generates decision trees from data examples. Experiments show that the classification accuracies of the different decision trees produced by the different methods under comparison are equal.
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In the default setting, the cost equals 1 for all the attributes. Thus, the disjointness criterion is treated as the first criterion of the AQDT-1 and AQDT-2 methods in the decision tree building experiments throughout this paper.
References
I. F. Imam, R.S. Michalski, Should decision trees be learned from examples of from decision rules? Source Lecture Notes in Computer Science, in Proceedings of the 7th International Symposium on Methodologies, vol. 689. (Springer, Berlin/Heidelberg, 1993), pp. 395–404
J.R. Quinlan, Discovering rules by induction from large collections of examples, in Expert Systems in the Microelectronic Age, ed. by D. Michie (Edinburgh University Press, Scotland, 1979), pp. 168–201
I. H. Witten, B.A. MacDonald, Using concept learning for knowledge acquisition, Int. J. Man Mach. Stud. 27(4), 349–370 (1988)
R.S. Michalski, I.F. Imam, Learning problem-oriented decision structures from decision rules: the AQDT-2 system, Lecture Notes in Artificial Intelligence, in Proceedings of 8th International Symposium Methodologies for Intelligent Systems, vol. 869 (Springer Verlag, Heidelberg, 1994), pp. 416–426
Y. Akiba, S. Kaneda, H. Almuallim, Turning majority voting classifiers into a single decision tree, in Proceedings of the 10th IEEE International Conference on Tools with Artificial Intelligence, 1998, pp. 224–230
Y. Chen, L.T. Hung, Using decision trees to summarize associative classification rules. Expert Syst. Appl. Pergamon Press, Inc. Publisher, 2009, 36(2), 2338–2351
R.S. Michalski, K. Kaufman, The aq19 system for machine learning and pattern discovery: a general description and user’s guide, Reports of the Machine Learning and Inference Laboratory, MLI 01-2, George Mason University, Fairfax, VA, 2001
J. Wojtusiak, AQ21 user’s guide. Reports of the Machine Learning and Inference Laboratory, MLI 04-5, George Mason University, 2004
R.S. Michalski, I.F. Imam, On learning decision structures. fundamenta informaticae 31(1),: 49–64 (1997)
R.S. Michalski, I. Mozetic, J. Hong, N. Lavrac, The multi-purpose incremental learning system AQ15 and its testing application to three medical domains, in Proceedings of AAAI-86, Philadelphia, PA, 1986, pp. 1041–1045
F. Bergadano, S. Matwin, R.S. Michalski, J. Zhang, Learning two-tiered descriptions of flexible concepts: the POSEIDON system. Mach. Learning 8(1), pp. 5–43 (1992)
A. Abdelhalim, I. Traore, The RBDT-1 method for rule-based decision tree generation, Technical report #ECE-09-1, ECE Department, University of Victoria, PO Box 3055, STN CSC, Victoria, BC, Canada, July 2009
A. Asuncion, D.J. Newman, UCI machine learning repository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. University of California, School of Information and Computer Science, Irvine, CA, 2007
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Abdelhalim, A., Traore, I. (2010). RBDT-1 Method: Combining Rules and Decision Tree Capabilities. In: Ao, SI., Rieger, B., Amouzegar, M. (eds) Machine Learning and Systems Engineering. Lecture Notes in Electrical Engineering, vol 68. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9419-3_40
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DOI: https://doi.org/10.1007/978-90-481-9419-3_40
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