Research Article
Attribute-Associated Neuron Modeling and Missing Value Imputation for Incomplete Data
Algorithm 1
The imputation based on ACFM and UMVDT.
INPUT: complete dataset , missing rate, ACFM, learning rate , maximum rounds T. | OUTPUT: the imputation error of at specified missing rate. | Generate an incomplete dataset according to specified missing rate. | Initialize missing values as variables, model weights, and thresholds. | Set t =0, precision =1. | while t<T and precision<0.001 do. | | for x in : | Input x into model and get output y. | Calculate the error for updating the model parameters and missing value variables respectively. | end for | Reconstruct model output and predict missing values. | Calculate the imputation error and precision. | end while | Output the imputation error. |
|