Feature selection for reduction of tabular knowledge-based systems

Information Technology and Management

Volumn 6, Issue 4, 2005, Pages 351-362

  Piramuthu, Selwyn ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

Feature selection; Machine learning; Tabular knowledge based system

Indexed keywords

EID: 27644450127     PISSN: 1385951X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10799-005-3900-0     Document Type: Article

References (42)

1. A.R. Abdel-Khalik and K.M. El-Sheshai, Information choice and utilization in an experiment on default prediction, Journal of Accounting Research, Autumn (1980) 325-342.
2. K.J. Adams, D.A. Bell, L.P. Maguire and J. McGregor, Knowledge discovery from decision tables by the use of multiple-valued logic, Artificial Intelligence Review 19(2) (2003) 153-176. 10.1023/A:1022617312306
3. B. Baesens, R. Setiono, C. Mues and J. Vanthienen, Using neural network rule extraction and decision tables for credit-risk evaluation, Management Science 49(3) (2003) 312-329. 10.1287/mnsc.49.3.312.12739
4. J.M. Benitez, J.L. Castro, C.J. Mantas and F. Rojas, A neuro-fuzzy approach for feature selection, in Proceedings of IFSA World Congress and 20th NAFIPS International Conference 2 (2001) 1003-1008.
5. P.S. Bradley, O.L. Mangasarian and W.N. Street, Feature selection in mathematical programming, INFORMS Journal on Computing (1998) 10(2).
6. L. Breiman, J. Friedman, R. Olshen and C. Stone, Classification and Regression Trees, (Belmont, CA, Wadsworth, 1984).
7. B. Chambless and David Scarborough, Information-theoretic feature selection for a neural behavioral model, Proceedings of the International Joint Conference on Neural Networks (IJCNN-01) 2 (2001) 1443-1448.
8. F.M. Coetzee, Eric Glover, Steve Lawrence and C. Lee Giles, Feature selection in web applications by ROC inflections and powerset pruning, Proceedings of the Symposium on Applications and the Internet (2001) 5-14.
9. T.M. Cover, The best two independent measurements are not the two best, IEEE Transactions on Systems, Man, and Cybernetics SMC-4:1 (1974) 116-117.
10. P.A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach (Prentice-Hall: 1982).
11. J.D. Elashoff, R.M. Elashoff and G.E. Goldman, On the choice of variables in classification problems with dichotomous variables, Biometrika 54 (1967) 668-670. 6064031
12. T. Elomaa and E. Ukkonen, A Geometric Approach to Feature Selection, in Proceedings of the European Conference on Machine Learning (1994) 351-354.
13. A.P. Eremeev. On correctness of the production decision model based on the decision tables, Automation and Remote Control 62(10) (2001) 1608-1619. 10.1023/A:1012454214384
14. U. Gupta 1991 Validating and Verifying Knowledge-based Systems (IEEE Press Washington, D.C.)
15. Kira, K. and L.A. Rendell, A practical approach to feature selection, in Proceedings of the Ninth International Conference on Machine Learning (1992) 249-256.
16. J. Kittler, Mathematical methods of feature selection in pattern recognition, International Journal of Man-Machine Studies 7 (1975) 609-637.
17. H. Liu and H. Motoda (eds.), Feature Extraction, Construction and Selection: A Data Mining Perspective (Kluwer, 1998).
18. H. Liu and Rudy Setiono, Feature selection via discretization, IEEE Transactions on Knowledge and Data Engineering 9(4) (1997) 642-645. 10.1109/69.617056
19. W.S. Meisel 1972 Computer-Oriented Approaches to Pattern Recognition Academic Press New York
20. M. van Middelkoop, A.W.J. Borgers, and H.J.P. Timmermans, Modelling tourist destination choice using a decision table induction algorithm, Environment and Planning 35(9) (2003) 1669-1687. 10.1068/a35182
21. S. Murrell and R. Plant, On the validation and verification of production systems: A graph reduction approach. International Journal of Human-Computer Studies 44 (1996) 127-144. 10.1006/ijhc.1996.0007
22. S.B. Nadler jr., Hyperspaces of Sets (Marcel Dekker, 1978 New York).
23. T.A. Nguyen, Verifying Consistency of Production Systems, Proceedings of the Third Conference on Artificial Intelligence Applications (1987) 4-8.
24. R.M. O'Keefe and D.E. O'Leary, Expert system verification and validation: A survey and tutorial, Artificial Intelligence Review 7 (1993) 3-42. 10.1007/BF00849196
25. D. E. O'Leary, The relationship between errors and size in knowledge-based systems, International Journal of Human-Computer Studies 44 (1996) 171-185. 10.1006/ijhc.1996.0009
26. D.E. O'Leary (eds.) Special Issue on Verification and Validation issues in databases, Knowledge-based systems, and ontologies, International Journal of Intelligent Systems 16(3) 2001.
27. M. Pazzani, The influence of prior knowledge on concept acquisition: Experimental and computational results, Journal of Experimental Psychology: Learning, Memory and Cognition 17(3) (1991) 416-432.
28. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning About Data (Kluwer, 1992).
29. S. Piramuthu, The Hausdorff Distance Measure for Feature Selection in Learning Applications, Proceedings of the 32nd Hawaii International Conference on System Sciences, 1999.
30. S. Piramuthu, Feature construction for reduction of tabular knowledge-based systems, Information Sciences 168 (2004) 201-215.
31. A. D. Preece, R. Shinghal and A. Batarekh, Principles and practice in verifying rule-based systems, The Knowledge Engineering Review 7 (1992) 115-141.
32. P. Pudil, F. J. Ferri, J. Novovicova and J. Kittler, Floating search methods for feature selection with nonmonotonic criterion functions," IEEE 12th International Conference on Pattern Recognition-Vol. II (1994) 279-283.
33. Stearns, S.D., On selecting features for pattern classifiers, Third International Conference on Pattern Recognition, (1976), 71-75.
34. G.T. Toussaint, "Note on Optimal Selection of Independent Binary-valued Features for Pattern Recognition." IEEE Transactions on Information Theory, IT-17 (1971) 618.
35. J. Vanthienen, Knowledge Acquisition and Validation Using a Decision Table Engineering Workbench. World Congress of Expert Systems (1991) 1861-1868.
36. J. Vanthienen, A tool-supported approach to inter-tabular verification. Expert Systems with Applications 15 (1998) 277-285. 10.1016/ S0957-4174(98)00047-5
37. J. Vanthienen, C. Mues, G. Wets and K. Delaere, A tool-supported approach to inter-tabular verification, Expert systems with applications 15 (1998) 277-285. 10.1016/S0957-4174(98)00047-5
38. J. Vanthienen and G. Wets, From decision tables to expert system shells, Data and Knowledge Engineering 13 (1994) 265-282. 10.1016/ 0169-023X(94)00020-4
39. A. Vermesan and F. Coenen, Validation and Verification of Knowledge Based Systems - Theory, Tools and Practice, (Kluwer, 1999).
40. G. Wets, J. Vanthienen and H. Timmermans, Modeling decision tables from Data, Proceedings of The Second Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer-Verlag, 1998, 412-413.
41. W. Ziarko, Acquisition of hierarchy-structured probabilistic decision tables and rules from data, Expert Systems 20(5) (2003) 305-310. 10.1111/ 1468-0394.00255
42. N. Zlatareva, Truth maintenance systems and their application for verifying expert system knowledge bases, Artificial Intelligence Review (6) (1992) 67-110.