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International Journal of Mathematical Sciences and Computing(IJMSC)

ISSN: 2310-9025 (Print), ISSN: 2310-9033 (Online)

Published By: MECS Press

IJMSC Vol.3, No.2, Apr. 2017

Handling Numerical Missing Values Via Rough Sets

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Author(s)

Elsayed Sallam, T. Medhat, A.Ghanem, M. E. Ali

Index Terms

Rough sets;missing values;prediction;most common value

Abstract

Many existing industrial and research data sets contain missing values. Data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors, and incorrect measurements. It is usual to find missing data in most of the information sources used. Missing values usually appear as "NULL" values in the database or as empty cells in the spreadsheet table. Multiple ways have been used to deal with the problem of missing data. The proposed model presents rough set theory as a technique to deal with missing data. This model can handle the missing values for condition and decision attributes, the web application was developed to predict these values.

Cite This Paper

Elsayed Sallam, T. Medhat, A.Ghanem, M. E. Ali,"Handling Numerical Missing Values Via Rough Sets", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.3, No.2, pp.22-36, 2017.DOI: 10.5815/ijmsc.2017.02.03

Reference

[1]Ahmed Tariq Sadiq, Mehdi Gzar Duaimi, Samir Adil Shaker" Data Missing Solution Using Rough Set Theory and Swarm Intelligence"International Journal of Advanced Computer Science and Information Technology (IJACSIT), Vol. 2( 3), pp.1-16, 2012.

[2]Boby P. Mathew, Sunil Jacob John,"ON ROUGH TOPOLOGICAL SPACES", International Journal of Mathematical Archive, Vol.3(9), pp.3413-3421,2012.

[3]H. Nasiri, M. Mashinchi," Rough Set and Data Analysis in Decision Tables", Journal of Uncertain Systems, Vol.3(3), pp.232-240, 2008.

[4]Hala S. Own, Aboul Ella Hassanien, "Rough Wavelet Hybrid Image Classification Scheme", JCIT, Vol. 3(4), pp.65-75, 2008.

[5]Hulse Jason Van, Khoshgoftaar Taghi M, "Incomplete-Case Nearest Neighbor Imputation In Software Measurement", Vol. 259, pp. 596–610, 2014. 

[6]Mert Bal," Rough Sets Theory as Symbolic Data Mining Method: An Application on Complete Decision Table ", Information Science Letters, Vol. 2(1), pp.35-47, 2012.

[7]Michinori Nakata, Hiroshi Sakai, "Applying Rough Sets to Data Tables Containing Missing Values", Lecture Notes in Computer Science, Vol. 4585, pp. 181-191, 2007

[8]M.K. Sabu, G. Raju "Rough Set Approaches for Mining Incomplete Information Systems", Vol. 5227, pp.914-921,2008

[9]N. Senthilkumaran, R. Rajesh"A Study on Rough Set Theory for Medical Image Segmentation", International Journal of Recent Trends in Engineering, Vol.2( 2),pp.236-238,2009.

[10]Pawlak Z., "Rough Sets", International Journal of Information and Computer Sciences, Vol.11(5), pp.341-356, 1982

[11]Pawlak Z, "Rough set approach to multi-attribute decision analysis", European Journal of Operational Research, Vol. 72(3), pp. 443-459, 1994.

[12]Pawlak Z., "Rough Sets and Intelligent Data Analysis", Information Sciences, Vol.147, pp. 1–12, 2002.

[13]T. Medhat, "Prediction of missing values for decision attribute", International Journal of Information Technology and Computer Science, Vol. 4(11), pp.58-66, 2013.

[14]Roman W.WINIARSKI,"ROUGH SETS METHODS IN FEATURE REDUCTION AND CLASSIFICATION", Vol.11(3), pp.565-582, 2001.

[15]G.Vamsi Krishna,"Prediction of Rainfall Using Unsupervised Model based Approach Using K-Means Algorithm", International Journal of Mathematical Sciences and Computing (IJMSC), Vol.1(1), pp.11-20, 2015.