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An Effective Method to Estimate Missing Value for Heterogonous Dataset


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1 Centre for Computer Science and Applications, Dibrugarh University, Dibrugarh, Assam, India
     

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Missing value estimation renders a probable state in which it is required to predict a missing value in a data set. It gets induced due to various reasons but the deal is to find a value for that particular cell. To justify a conclusive result in contrast to the original value various techniques are used. It does not assure us to choose a specific method applying for a data set because different techniques will yield different result for different data set that may not trigger an authentic value. So we deployed a technique confined of various other methods in it by which we can choose any of the methods in it to acquire a missing values. The main goal of this paper is the technique we proposed by assembling different methods get an efficient value. We have applied the proposed method in 6 different data set and validate the result using 4 validation techniques that can accord us with a most precise value or result.

Keywords

Data Set, Methods, Missing Value, Techniques.
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  • A. Farhangfara, L. Kurganb, and J. Dy, “Impact of imputation of missing values on classification error for discrete data,” Pattern Recognition, vol. 41, no. 12, pp. 3692-3705, December 2008.

  • S. Singh, and J. Prasad, “Estimation of missing values in the data mining and comparison of imputation methods,” Mathematical Journal of Interdisciplinary Sciences, vol. 1, no. 2, pp. 75-90, March 2013.

  • Madhu G., and Nagachandrika G., “A new paradigm for development of data imputation approach for missing value estimation,” International Journal of Electrical and Computer Engineering (IJECE), vol. 6, no. 6, pp. 3222-3228, 2016.

  • O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, …...., and R. B. Altman, “Missing value estimation methods for DNA microarrays,” Bioinformatics, vol. 17, no. 6, pp. 520-525, June 2001.

  • P. Schmitt, J. Mandel, and M. Guedj, “A comparison of six methods for missing data imputation,” Journal of Biometrics and Biostatistics, vol. 6, no. 1, article 224, May 2015. DOI: 10.4172/2155-6180.1000224

  • S. Bhushan, and A. P. Pandey, “Optimal imputation of missing data for estimation of population mean,” Journal of Statistics and Management Systems, vol. 19, no. 6, pp. 755-769, 2016.

  • N. J. Perkins, S. R. Cole, O. Harel, E. J. Tchetgen Tchetgen, B. Sun, E. M. Mitchell, and E. F. Schisterman, “Principled approaches to missing data in epidemiologic studies,” American Journal of Epidemiology, vol. 187, no. 3, pp. 568-575, 2017.


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  • An Effective Method to Estimate Missing Value for Heterogonous Dataset

Abstract Views: 238  |  PDF Views: 0

Authors

Priyakshi Mahanta
Centre for Computer Science and Applications, Dibrugarh University, Dibrugarh, Assam, India
Gulzar Ahmed Choudhury
Centre for Computer Science and Applications, Dibrugarh University, Dibrugarh, Assam, India
Tapash Dey
Centre for Computer Science and Applications, Dibrugarh University, Dibrugarh, Assam, India

Abstract


Missing value estimation renders a probable state in which it is required to predict a missing value in a data set. It gets induced due to various reasons but the deal is to find a value for that particular cell. To justify a conclusive result in contrast to the original value various techniques are used. It does not assure us to choose a specific method applying for a data set because different techniques will yield different result for different data set that may not trigger an authentic value. So we deployed a technique confined of various other methods in it by which we can choose any of the methods in it to acquire a missing values. The main goal of this paper is the technique we proposed by assembling different methods get an efficient value. We have applied the proposed method in 6 different data set and validate the result using 4 validation techniques that can accord us with a most precise value or result.

Keywords


Data Set, Methods, Missing Value, Techniques.

References