Radial Basis Function Network Dependent Exclusive Mutual Interpolation for Missing Value Imputation

The success of data mining relies on the purity of the data set. Before performing the data mining, the data has to be cleaned. An unprocessed data set may contain noisy or missing values which is a critical research issue in the pre-processing stage. Imputation methods are being used to solve the missing value problems. In this proposed work, a machine learning based imputation method is proposed by using the mutual information by exclusively interpolating two different section of the same dataset. For designing the proposed model, a radial basis function based neural network has been used. The performance of the proposed algorithm has been measured with respect to different rate or percentage of missing values in the data set and the results has been compared with existing simple and efficient imputation methods also. To evaluate the performance, the standard WDBC data set has been used. The proposed algorithm performs well and was able to impute the missing values even in the worst cases with more than 50% of missing values. Instead of using simple quality measure such as Mean Square Error (MSE) to evaluate the imputed data quality, in this study, the quality is measured in terms of classification performance. The results arrived were more significant and comparable.