Abstract
Knowledge, in the form of rules, can enhance raw data by offering a compact summary or by giving a predictive capability. Frequently, information in a database may be incomplete or uncertain; however, it is often possible to estimate the value of missing data by comparison to similar cases in the database. Humans usually prefer to work in terms of rules which summarise trends in data, rather than remembering specific details from all cases. These rules may not be completely reliable, and may not allow the original data to be completely reproduced; however, they can greatly compress the data and often give insight into the underlying structure of the data.
The Fril data browser can form rules which predict the unknown values in a database from the known values in that particular case, plus the values in other similar cases. The database is partitioned into fuzzy subsets containing similar values of the variable under consideration; each rule uses fuzzy sets to summarise values of other variables in that partition. These rules can be inspected and understood easily by humans, and can be adjusted in the light of expert knowledge.
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References
Baldwin, J.F. (1991a). “Combining Evidences for Evidential Reasoning.” International Journal of Intelligent Systems. (6) pp 569–616.
Baldwin, J.F. (1991b) “A Calculus for Mass Assignments in Evidential Reasoning” In Advances in the Dempster-Shafer Theory of Evidence. Eds Fedrizzi, M, Kacprzyk, J. & Yager, R.R.
Baldwin, J. F. (1992). “The Management of Fuzzy and Probabilistic Uncertainties for Knowledge Based Systems” in Encyclopedia of AI, Ed. S. A. Shapiro, John Wiley. (2nd ed.) 528–537.
Baldwin, J. F. (1993a). “Evidential Support Logic, FRIL and Case Based Reasoning.” International Journal of Intelligent Systems8(9): 939–961.
Baldwin, J. F. (1993b). “Fuzzy, Probabilistic and Evidential Reasoning in Fril”, Proc. 2nd IEEE International Conference on Fuzzy Systems, San Francisco, CA, 459–464. (ISBN 0-7803-0614-7).
Baldwin, J. F. (1995). “Fril Methods for Soft Computing, Fuzzy Control, and Classification”, Proc. 4th IEEE International Conference on Fuzzy Systems, Yokohama, Japan, 309–316.
Baldwin, J. F. and Martin, T. P. (1994). “Fuzzifying a Target Motion Analysis Model Using Fril and Mathematica”, Proc. 3rd IEEE International Conference on Fuzzy Systems, Florida, 1171–1175.
Baldwin, J. F. and Martin, T. P. (1995). “Refining Knowledge from Uncertain Relations — a Fuzzy Data Browser based on Fuzzy Object-Oriented Programming in Fril”, Proc. 4th IEEE International Conference on Fuzzy Systems, Yokohama, Japan, 27–34.
Baldwin, J. F., Martin, T. P. and Pilsworth, B. W. (1993). “Fril: A Support Logic Programming System” in AI and Computer Power: The Impact on Statistics, Ed. D. Hand, Chapman and Hall. 129–149.
Baldwin, J. F., Martin, T. P. and Pilsworth, B. W. (1995). “FRIL-Fuzzy and Evidential Reasoning in AI”, Research Studies Press (John Wiley).
Dubois, D. and Prade, H. (1991). “Fuzzy sets in approximate reasoning 1 — inference with possibility distributions.” Fuzzy Sets and Systems40: 143–202.
Gelhar, L. W., Welty, C. and Rehfeldt, K. R. (1992). “A Critical Review of Data inField-Scale Dispersion in Aquifers.” Water Resources Research28(7): 1955–1974.
Sudkamp, T. (1992). “On Probability-Possibility Transformations” Fuzzy Sets and Systems (51) pp 73–81.
Wolfram, S. (1991). “Mathematica: a system for doing mathematics by computer”, Addison Wesley.
Yager,R.R.(1984) “On Different Classes of Linguistic Variables defined via Fuzzy Subsets.” Kybernetes13:103–10
Zadeh, L.A. (1968). “Probability Measures of Fuzzy Events” Journal of Mathematical Analysis and Applications. (23). 421–427.
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© 1997 Springer-Verlag Berlin Heidelberg
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Baldwin, J.F., Martin, T.P. (1997). Extracting knowledge from data using an intelligent fuzzy data browser. In: Martin, T.P., Ralescu, A.L. (eds) Fuzzy Logic in Artificial Intelligence Towards Intelligent Systems. FLAI 1995. Lecture Notes in Computer Science, vol 1188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62474-0_7
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DOI: https://doi.org/10.1007/3-540-62474-0_7
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