Abstract
This chapter presents a “divide-and-conquer” data analysis method that, given a concept described by a decision table, develops its description in terms of intermediate concepts described by smaller and more manageable decision tables. The method is based on decision table decomposition, a machine learning approach that decomposes a given decision table into an equivalent hierarchy of decision tables. The decomposition aims to discover the decision tables that are overall less complex than the initial one, potentially easier to interpret, and introduce new and meaningful intermediate concepts. The chapter introduces the decomposition method and, through decomposition-based data analysis of two neurophysiological datasets, shows that the decomposition can discover physiologically meaningful concept hierarchies and construct interpretable decision tables which reveal relevant physiological principles.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ashenhurst, R. L. (1952). The decomposition of switching functions. Technical report, Bell Laboratories BL-1(11), pages 541–602.
Biermann, A. W., Fairfield, J., and Beres, T. (1982). Signature table systems and learning. IEEE Trans. Syst. Man Cybern., 12(5):635–648.
Bohanec, M., Bratko, I., and Rajkovic, V. (1983). An expert system for decision making. In Sol, H. G., editor, Processes and Tools for Decision Support. North-Holland.
Bohanec, M. and Rajkovič, V. (1990). DEX: An expert system shell for decision support. Sistemica, 1(1):145–157.
Buchanan, G. and Shortliffe, E. H., editors (1984). Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading, Mass.
Curtis, H. A. (1962). A New Approach to the Design of Switching Functions. Van Nostrand, Princeton, N.J.
Demšar, J., Zupan, B., Bohanec, M., and Bratko, I. (1997). Constructing intermediate concepts by decomposition of real functions. In Proc. European Conference on Machine Learning, ECML-96, pages 93–107. Springer Verlag.
Fu, L.-M. and Buchanan, B. G. (1985). Learning intermediate concepts in constructing a hierarchical knowledge base. In Proc. of International Joint Conference on Artificial Intelligence IJCAI-85, pages 659–666, Los Angeles, CA.
Goldman, J. A., Ross, T. D., and Gadd, D. A. (1995) Pattern Theoretic Learning. In AAAI Spring Symposium Series: Systematic Methods of Scientific Discovery.
Halter, J. A., Carp, J. S., and Wolpaw, J. W. (1995). Operantly conditioned motoneuron plasticity: Possible role of sodium channels. J. Neurophysiology, 73(2):867–871.
Halter, J. A. and Clark, J. W. (1991). A distributed-parameter model of the myelinated nerve fiber. J. Theo. Biol., 148:345–382.
Levine, D. (1995). User’s guide to PGAPack parallel genetic algorithm library. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL.
Luba, T. (1995). Decomposition of multiple-valued functions. In 25th Intl. Symposium on Multiple-Valued Logic, pages 256–261, Bloomigton, Indiana.
Michalski, R. S. (1986). Understanding the nature of learning: Issues and research directions. In Michalski, R., Carbonnel, J., and Michell, T., editors, Machine Learning: An Artificial Intelligence Approach, pages 3–25. Kaufmann, Los Atlos, CA.
Michie, D. (1995). Problem decomposition and the learning of skills. In Lavrac, N. and Wrobel, S., editors, Machine Learning: ECML-95, Notes in Artificial Intelligence 912, pages 17–31. Springer Verlag.
Perkowski, M. A. (1995). A survey of literature on function decomposition. Technical report, GSRP Wright Laboratories, Ohio OH.
Pfahringer, B. (1994). Controlling constructive induction in CiPF. In Bergadano, F. and Raedt, L. D., editors, Machine Learning: ECML-94, pages 242–256. Springer Verlag.
Phillips, M. (1994). Geomview manual. The Geometry Center, University of Minnesota.
Ragavan, H. and Rendell, L. (1993). Lookahead feature construction for learning hard concepts. In Proc. Tenth International Machine Learning Conference, pages 252–259. Morgan Kaufman.
Samuel, A. (1967). Some studies in machine learning using the game of checkers II: Recent progress. IBM J. Res. Develop., 11:601–617.
Shapiro, A. D. (1987). Structured Induction in Expert Systems. Turing Institute Press in association with Addison-Wesley Publishing Company.
Stahl, I. (1991). An overview of predicate invention techniques in ILP. In Project Report ESPRIT BRA 6020: Inductive Logic Programming.
Zupan, B., Bohanec, M., Bratko, I., and Demšar, J. (1997). Machine learning by function decomposition. In Proc. 14th International Conference on Machine Learning, Nashville, TN.
Zupan, B., Halter, J. A., and Bohanec, M. (1995). Computer-assisted reasoning on principles and properties of medical physiology. In Proc. of the Workshop on Computer-Aided Data Analysis in Medicine, CADAM-95, pages 258-271, Bled.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer Science+Business Media New York
About this chapter
Cite this chapter
Zupan, B., Halter, J.A., Bohanec, M. (1997). Concept Discovery by Decision Table Decomposition and its Application in Neurophysiology. In: Lavrač, N., Keravnou, E.T., Zupan, B. (eds) Intelligent Data Analysis in Medicine and Pharmacology. The Springer International Series in Engineering and Computer Science, vol 414. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6059-3_15
Download citation
DOI: https://doi.org/10.1007/978-1-4615-6059-3_15
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7775-7
Online ISBN: 978-1-4615-6059-3
eBook Packages: Springer Book Archive