Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Nowadays, the missing data imputation is the novel paradigm to replace with the imputed value ofthe missing attribute.The missing data occurs due to bias information, non-response of the system. Inthemedical 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 inthemixed 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.

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

VIT Unitversity

International Journal of Intelligent Engineering and Systems

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

This paper proposes a novel approach, grey neural network (GNN) that is composed of Levenberg-Marquardt neural network and grey wolf optimiser. The WLI fuzzy clustering mechanism predicts the data by clustering the data into groups, and the neural network trains the missing attribute in the dataset. The Levenberg-Marquardt neural network is trained based on the grey wolf optimiser that determines the optimal weight. Finally, the two imputed values are combined significantly to impute the data where the missing data occurs. Experimentation using the medical dataset proves the accuracy of the proposed hybrid model and the results of the proposed GNN are compared with the existing methods like KNN, WLI and GWLMN. The proposed method exhibits a good efficiency with minimum values of MSE and RMSE compared to the existing methods. This method also attains a minimum RMSE of 0.11 which ensures the efficient data imputation.

International Journal of Intelligent Enterprise

Integrating WLI fuzzy clustering with grey neural network for missing data imputation

The missing data and noisy data are common in a data set and the finding the effect it causes on the accuracy is very important to be determined. In statistics, missing data, or such values, occur when no data value is assigned for a field in a dataset. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data given or taken from warehouses. Missing data reduce the representativeness of the sample and can therefore distort/deviate inferences &amp; conclusions about the population. This study aims at calculating the effect of missing values on Na�ve Bayes algorithm by using two data sets that are lymphoma and breast cancer. The values are skipped in certain order of both the data set and accuracy is computed and results were compared in a table. Na�ve Bayes is based on probalistic model.

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International Journal of Engineering and Technology

An Analysis and Comparison of Various Missing Data Imputation Tools and Techniques

Ongoing research and development process in medical data mining have opened up versatile computer assisted approaches for effective clinical decisions. The nature and quality of the selected sample for training is largely responsible for the performance of the data mining algorithms. The large quantities of cumulative data collected from various sources suffer from qualitative deficiency factors such as inconsistency, incompleteness and redundancy. Addressing the prime problem of missing data is vital as it may introduce a bias into the model under evaluation, at times leading to inaccurate results. Imputation of missing data through instance-based clustering methodology is proposed in this paper. A complete dataset, Pima Indian Type II Diabetes, is considered for evaluation of the proposed method and its usefulness and performance are estimated through average imputation error (E). The results illustrate that the proposed clustering method gives a lesser and stable error rate compared to other existing imputation methods. Copyright © 2014 Inderscience Enterprises Ltd.