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DOI: http://dx.doi.org/10.22266/ijies2017.0430.16

Grey Fuzzy Neural Network-Based Hybrid Model for Missing Data Imputation in Mixed Database

Author(s):

Vijayakumar Kuppusamy1*, Ilango Paramasivam1


Affiliations:

1 School of Computer Science and Engineering, Vellore Institute of Technology University, Vellore, India







Abstract:

Nowadays, the missing data imputation is the novel paradigm to replace with the imputed value of the missing attribute. The missing data occurs due to bias information, non-response of the system. In the medical domain, it becomes the major challenge to impute the both categorical and numerical data. In this paper, the Grey Fuzzy Neural Network is proposed for missing data imputation in the mixed database. Initially, the WLI fuzzy clustering mechanism is utilized to generate the different clusters in which the medical data are grouped together. Then, we intend to integrate the Grey Wolf Optimizer (GWO) with the ANFIS network model, termed the Grey Fuzzy Neural Network (GFNN). The proposed method is mainly used to determine the optimal parameters to design the membership function. Finally, the hybrid prediction model is used to find out the imputed data for both categorical and numerical. In the hybrid prediction model, the categorical data is then imputed by the distance measure. The experimental results are validated, and performance is analysed by metrics such as MSE and RMSE using MATLAB implementation. The outcome of the proposed GFNN attains lower 0.13 MSE, and 0.35 RMSE ensures to impute the data significantly in the missing attribute of the mixed database.


Keywords:

Categorical and Numerical missing data, WLI fuzzy clustering, Grey Wolf Optimizer, ANFIS, Hybrid prediction model.


Full Text:




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