A closest fit approach to missing attribute values in preterm birth data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1711, Issue , 1999, Pages 405-413

  Grzymala Busse, Jerzy W ^a   Grzymala Busse, Witold J ^b   Goodwin, Linda K ^c  

^a UNIVERSITY OF KANSAS   (United States)

^b RS Systems Inc   (United States)

^c DUKE UNIVERSITY   (United States)

Author keywords

Classification of unseen cases; Closest fit; Data mining; Missing attribute values; Rough set theory; Rule induction; System OOMIS

Indexed keywords

COMPUTATION THEORY; DATA MINING; SET THEORY; SOFT COMPUTING;

ATTRIBUTE VALUES; CLOSEST FITS; DUKE UNIVERSITY; MEDICAL CENTER; NEW APPROACHES; RULE INDUCTION; RULE INDUCTION METHODS; SYSTEM OOMIS;

ROUGH SET THEORY;

EID: 84958038952     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-48061-7_49     Document Type: Conference Paper

References (16)

1. Bairagi, R. and Suchindran C.M.: An estimator of the cutoff point maximizing sum of sensitivity and specificity. Sankhya, Series B, Indian Journal of Statistics 51 (1989) 263–269.
2. Booker, L. B., Goldberg, D. E., and Holland, J. F.: Classifier systems and genetic algorithms. In Machine Learning. Paradigms and Methods. Carbonell, J. G. (ed.), The MIT Press, 1990, 235–282.
3. Grzymala-Busse, J. W.: On the unknown attribute values in learning from examples. Proc. of the ISMIS-91, 6th International Symposium on Methodologies for Intelligent Systems, Charlotte, North Carolina, October 16–19, 1991, 368–377, Lecture Notes in Artificial Intelligence, vol. 542, 1991, Springer-Verlag.
4. Grzymala-Busse, J. W.: LERS—A system for learning from examples based on rough sets. In Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory. Slowinski, R. (ed.), Kluwer Academic Publishers, 1992, 3–18.
5. Grzymala-Busse, J.W. and Goodwin, L.K.: Predicting preterm birth risk using machine learning from data with missing values. Bull. of Internat. Rough Set Society 1 (1997) 17–21.
6. Grzymala-Busse, J. W.: LERS—A knowledge discovery system. In Rough Sets in Knowledge Discovery 2, Applications, Case Studies and Software Systems, ed. by L. Polkowski and A. Skowron, Physica-Verlag, 1998, 562–565.
7. Grzymala-Busse, J.W. and Wang A.Y.: Modified algorithms LEM1 and LEM2 for rule induction from data with missing attribute values. Proc. of the Fifth International Workshop on Rough Sets and Soft Computing (RSSC’97) at the Third Joint Conference on Information Sciences (JCIS’97), Research Triangle Park, NC, March 2–5, 1997, 69–72.
8. Grzymala-Busse, J.W. and Zou X.: Classification strategies using certain and possible rules. Proc. of the First International Conference on Rough Sets and Current Trends in Computing, Warsaw, Poland, June 22–26, 1998. Lecture Notes in Artificial Intelligence, No. 1424, Springer Verlag, 1998, 37–44.
9. Grzymala-Busse, J. W., Goodwin, L.K., and Zhang, X.: Increasing sensitivity of preterm birth by changing rule strengths. Submitted for the 8th Workshop on Intelligent Information Systems (IIS’99), Ustronie, Poland, June 14–18, 1999.
10. Holland, J. H., Holyoak K. J., and Nisbett, R. E.: Induction. Processes of Inference, Learning, and Discovery. The MIT Press, 1986.
11. Michalski, R. S., Mozetic, I., Hong, J. and Lavrac, N.: The AQ15 inductive learning system: An overview and experiments. Department of Computer Science, University of Illinois, Rep. UIUCDCD-R-86-1260, 1986.
12. Pawlak, Z.: Rough sets. International Journal Computer and Information Sciences 11 (1982) 341–356.
13. Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, 1991.
14. Stefanowski, J.: On rough set based approaches to induction of decision rules. In Polkowski L., Skowron A. (eds.) Rough Sets in Data Mining and Knowledge Discovery, Physica-Verlag, 1998, 500–529.
15. Swets, J.A. and Pickett, R.M.: Evaluation of Diagnostic Systems. Methods from Signal Detection Theory. Academic Press, 1982.
16. Ziarko, W.: Systems: DataQuest, DataLogic and KDDR. Proc. of the Fourth Int. Workshop on Rough Sets, Fuzzy Sets and Machine Discovery RSFD’96, Tokyo, Japan, November 6–8, 1996, 441–442.