Estimation of Incomplete Data in Mixed Dataset

This paper puts forward a fresh approach which is a modification of original fuzzy kNN for dealing with categorical missing values in categorical and mixed attribute datasets. We have removed the irrelevant missing samples through list-wise deletion. Then, rest of the missing samples is estimated using kernel-based fuzzy kNN technique and partial distance strategy. We have calculated the errors at different percentage of missing values. Results highlight that mixture kernel gives minimum average of MAE, MAPE and RMSE at different missing percentage when implemented on lenses, SPECT heart and abalone dataset.