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| B.R. Gaines, P. Compton, "Induction of Meta-Knowledge About Knowledge Discovery," IEEE Transactions on Knowledge and Data Engineering, vol. 5, no. 6, pp. 990-992, December, 1993. | |||
| BibTex | x | ||
| @article{ 10.1109/69.250084, author = {B.R. Gaines and P. Compton}, title = {Induction of Meta-Knowledge About Knowledge Discovery}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {5}, number = {6}, issn = {1041-4347}, year = {1993}, pages = {990-992}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.250084}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Knowledge and Data Engineering TI - Induction of Meta-Knowledge About Knowledge Discovery IS - 6 SN - 1041-4347 SP990 EP992 EPD - 990-992 A1 - B.R. Gaines, A1 - P. Compton, PY - 1993 KW - meta-knowledge induction; knowledge discovery; ripple-down rule induction; metamodel; clinical data; expert system; thyroid diagnosis; inductive knowledge discovery; real-world data; error rates; Garvan thyroid database; induct; machine learning; medical diagnosis; meta-modeling; rules with exceptions; inference mechanisms; learning (artificial intelligence); medical administrative data processing; medical diagnostic computing; medical expert systems VL - 5 JA - IEEE Transactions on Knowledge and Data Engineering ER - | |||
A study is reported of the use of ripple-down rule induction to develop a metamodel of ten years of clinical data captured as part of the development of an expert system for thyroid diagnosis. It is shown how the suitability for inductive knowledge discovery from such real-world data can be characterized in terms of its stationarity, and how the best error rates achievable and the amount of data necessary to achieve them can be estimated.
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[2] P. Compton and R. Jansen, "A philosophical basis for knowledge acquisition,"Knowledge Acquisition, vol. 2, no. 3, pp. 241-258, 1990.
[3] B. R. Gaines, "An ounce of knowledge is worth a ton of data: Quantitative studies of the trade-off between expertise and data based on statistically well-founded empirical induction," inProc. 6th Int. Workshop on Machine Learning, 1989, pp. 156-159.
[4] B. R. Gaines and P. Compton, "Induction of ripple-down rules," In: A. Adams and L. Sterling (eds.): AI'92. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence. Hobart, Tasmania, World Scientific, Singapore 1992, pp. 349-354.
[5] P. J. Horn, P. J. Compton, L. Lazarus, and J. R. Quinlan, "An ex-Pert system for the interpretation of thyroid assays in a clinical laboratoty,"Australian Comput. J., vol. 17, pp. 7-11, 1985.
[6] D. Hume,A Treatise of Human Nature. Oxford, England: Clarendon, 1888.
[7] Xiaofeng Li, "What's So Bad About Rule-Based Programming,"IEEE Software, Sept. 1991, pp. 103, 105.
[8] Y. Mansuri, J. G. Kim, P. Compton, and C. Sammut, "A comparison of a manual knowledge acquisition method and an inductive learning method, " inProc. 1st Australian Workshop Knowledge Acquisition for Knowledge-Based Systems. Sydney : Univ. Sydney, 1991, pp. 114-132.
[9] G. Piatetsky-Shapiro and W. Frawley,Knowledge Discovery in Databases. Menlo Park, CA: AAAI Press/MIT Press, 1991.
[10] J. Quinlan, "Simplifying decision trees,"Int. J. Man-Machine Studies, vol. 27, pp. 221-234, 1987.

