Handling missing attribute values in preterm birth data sets

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

Volumn 3642 LNAI, Issue , 2005, Pages 342-351

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

^a University of Kansas   (United States)

^b INSTITUTE OF COMPUTER SCIENCE   (Poland)

^c DUKE UNIVERSITY   (United States)

^d Filterlogix   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CODES (SYMBOLS); DATA REDUCTION; INFORMATION SCIENCE; NUMERICAL ANALYSIS;

DATA SETS; MISSING ATTRIBUTE VALUES; NUMERICAL ATTRIBUTES; SPECIFIED INFORMATIONS; SYMBOLIC ATTRIBUTES;

DATA HANDLING;

EID: 33645959851     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11548706_36     Document Type: Conference Paper

References (8)

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. 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.
3. Grzymala-Busse, J.W.:. MLEM2: A new algorithm for rule induction from imperfect data. Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2002, July 1-5, Annecy, France, 243-250.
4. Grzymala-Busse, J. W., Grzymala-Busse, W. J. and Goodwin, L. K.: A closest fit approach to missing attribute values in preterm birth data. Proc. of the Seventh Int. Workshop on Rough Sets, Fuzzy Sets, Data Mining and Granular-Soft Computing (RSFDGrC'99), Ube, Yamaguchi, Japan, November 8U″10, 1999. Lecture Notes in Artificial Intelligence, No. 1711, Springer Verlag, 1999, 405-413.
5. 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.
6. Grzymala-Busse, J. W., Goodwin, L.K., and Zhang, X.: Increasing sensitivity of preterm birth by changing rule strengths. Proceedings of the 8th Workshop on Intelligent Information Systems (IIS'99), Ustronie, Poland, June 14-18, 1999, 127-136.
7. McLean, M., Walters, W. A. and Smith, R.:. 1993. Prediction and early diagnosis of preterm labor: a critical review. Obstetrical & Gynecological Survey 48 (1993) 209-225.
8. Swets, J.A. and Pickett, R.M.: Evaluation of Diagnostic Systems. Methods from Signal Detection Theory. Academic Press, 1982.