Using fuzzy c means and multi layer perceptron for data imputation: Simple v/s complex dataset
Shambeel Azim, Swati Aggarwal · 2016
Data imputation is the process of filling in the missing value to generate complete records. Complete databases can be analyzed more accurately in comparison to incomplete databases. This paper implements a 2-stage hybrid model for filling in the missing values. Also the effect of the proposed model over simple and complex dataset with varying percentage of missing value and varying value of fuzzifier is evaluated. The accuracy of the model is checked with Mean Absolute Percentage Error (MAPE). The result obtained shows that the proposed model is more accurate in filling multiple values in a record compared to stage 1 alone.