Prediction of Missing Values for Decision Attribute

Full Text (PDF, 643KB), PP.58-66

Views: 0 Downloads: 0


T. Medhat 1,*

1. Computer and Automatic Control Department, Faculty of Engineering, Kafrelsheikh University, 33516, Kafrelsheikh, Egypt

* Corresponding author.


Received: 14 Feb. 2012 / Revised: 5 Jun. 2012 / Accepted: 23 Aug. 2012 / Published: 8 Oct. 2012

Index Terms

Rough Sets, Degree of Dependency, Distance Function, Missing Values


The process of determining missing values in information system is an important issue for decision making especially when the missing values are in the decision attribute. The main goal for this paper is to introduce algorithm for finding missing values of decision attribute. Our approach is depending on distance function between existing values. These values can be calculated by distance function between the conditions attributes values for the complete information system and incomplete information system. This method can deal with the repeated small distance by eliminating a condition attribute which has the smallest effect on the complete information system. This algorithm will be discussed in detail with an example of a case study.

Cite This Paper

T. Medhat, "Prediction of Missing Values for Decision Attribute", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.11, pp.58-66, 2012. DOI:10.5815/ijitcs.2012.11.08


[1]Bazan J., "A Comparison of dynamic and nondynamic rough set methods for extracting laws from decision tables", Rough Sets in Knowledge Discovery, Physica Verlag, 1998.

[2]Hala S. Own, Aboul Ella Hassanien, "Rough Wavelet Hybrid Image Classification Scheme", JCIT, Vol. 3, No. 4, pp. 65 ~ 75, 2008.

[3]Hu K.Y., Lu Y.C., Shi C.Y., "Feature ranking in rough sets", AI Commun. 16 (1), 41~50, 2003.

[4]Hu, X., Cercone N., Han, J., Ziarko, W, "GRS: A Generalized Rough Sets Model", in Data Mining, Data Mining, Rough Sets and Granular Computing, T.Y. Lin, Y.Y.Yao and L. Zadeh (eds), Physica-Verlag, 447~ 460, 2002.

[5]Jin-Cherng Lin and Kuo-Chiang Wu, "Using Rough Set and Fuzzy Method to Discover the Effects of Acid Rain on the Plant Growth", JCIT, Vol. 2, No. 1, pp. pp ~ 48, 2007.

[6]Komorowski J., Ohrn A., "Modelling prognostic power of cardiac tests using rough sets", Artif. Intell. Med. 15, 167~191, 1999.

[7]Lashin E.F, Kozae A.M., Abo Khadra A.A., and Medhat T., "Rough set theory for topological spaces", International Journal of Approximate Reasoning, Vol. 40, No. 1-2, 35~43, 2005.

[8]Li G.Z., Yang J., Ye C.Z., Geng D.Y., "Degree prediction of malignancy in brain glioma using support vector machines", Comput. Biol. Med. Vol. 36, No. 3, 313~325, 2006.

[9]Lin T.Y., "Granular computing on binary relations I: data mining and neighborhood systems, II: rough set representations and belief functions", In: Rough Sets in Knowledge Discovery , Lin T.Y., Polkowski L., Skowron A., (Eds.). Physica-Verlag, Heidelberg ,107~140, 1998.

[10]Lin T.Y., Yao Y.Y., Zadeh L.A., (Eds.) " Rough Sets, Granular Computing and Data Mining", Physica-Verlag, Heidelberg, 2002.

[11]Medhat T., "Missing Values Via Covering Rough Sets", IJMIA: International Journal on Data Mining and Intelligent Information Technology Applications, Vol. 2, No. 1, pp. 10 ~ 17, 2012.

[12]Pawlak Z., "Rough set approach to multi-attribute decision analysis", European Journal of Operational Research, Vol. 72, No. 3, 443~459, 1994.

[13]Pawlak Z., "Rough Sets - Theoretical Aspects of Reasoning about data.", Kluwer Academic Publishers, Dordrecht, Boston, London, 1991. 

[14]Shifei Ding, Yu Zhang, Li Xu, Jun Qian, "A Feature Selection Algorithm Based on Tolerant Granule", JCIT, Vol. 6, No. 1, pp. 191 ~ 195, 2011.

[15]Tsumoto S., "Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model", Inform. Sci. Vol. 162, 65~80, 2004.