Abstract
The objective of our research was to find the best approach to handle missing attribute values in data sets describing preterm birth provided by the Duke University. Five strategies were used for filling in missing attribute values, based on most common values and closest fit for symbolic attributes, averages for numerical attributes, and a special approach to induce only certain rules from specified information using the MLEM2 approach. The final conclusion is that the best strategy was to use the global most common method for symbolic attributes and the global average method for numerical attributes.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bairagi, R., Suchindran, C.M.: An estimator of the cutoff point maximizing sum of sensitivity and specificity. Sankhya, Series B, Indian Journal of Statistics 51, 263–269 (1989)
Grzymala-Busse, J.W.: LERS—A system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)
Grzymala-Busse, J.W.: MLEM2: A new algorithm for rule induction from imperfect data. In: Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2002, Annecy, France, July 1-5, pp. 243–250 (2002)
Grzymala-Busse, J.W., Grzymala-Busse, W.J., Goodwin, L.K.: A closest fit approach to missing attribute values in preterm birth data. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), pp. 405–413. Springer, Heidelberg (1999)
Grzymala-Busse, J.W., Zou, X.: Classification strategies using certain and possible rules. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 37–44. Springer, Heidelberg (1998)
Grzymala-Busse, J.W., Goodwin, L.K., Zhang, X.: Increasing sensitivity of preterm birth by changing rule strengths. In: Proceedings of the 8th Workshop on Intelligent Information Systems (IIS 1999), Ustronie, Poland, June 14–18, pp. 127–136 (1999)
McLean, M., Walters, W.A., Smith, R.: Prediction and early diagnosis of preterm labor: a critical review. Obstetrical & Gynecological Survey 48, 209–225 (1993)
Swets, J.A., Pickett, R.M.: Evaluation of Diagnostic Systems. Methods from Signal Detection Theory. Academic Press, Methods from (1982)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grzymala-Busse, J.W., Goodwin, L.K., Grzymala-Busse, W.J., Zheng, X. (2005). Handling Missing Attribute Values in Preterm Birth Data Sets. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_36
Download citation
DOI: https://doi.org/10.1007/11548706_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
Online ISBN: 978-3-540-31824-8
eBook Packages: Computer ScienceComputer Science (R0)