An approach towards missing data management using improved GRNN-SGTM ensemble method

The paper considers missing data management task in smart systems. The main strategies of missing data management in handling missing data are analyzed. A prediction method for probable recovery of partially missing or completely lost data based on the improvement of an ensemble of two GRNNs by the additional use of extended-input SGTM neural-like structure is proposed. The latter is used to increase the accuracy of the procedure of weighted summation with the displacement of the outputs of both GRNN networks in comparison with existing methods. The flowchart of the ensemble is given. The training algorithm is presented, as well as the detailed procedure of its use. The improved ensemble prediction method has been tested for the task of completing the gaps in the real dataset reflecting air condition monitoring. The optimal parameters of the component operation of the improved ensemble have been determined experimentally. The efficiency of its performance has been evaluated by its experimental comparison with methods of the same class based on the MAPE and RMSE accuracy indicators. The minimum value of an application error of the developed method for solving the task in comparison with existing ones for the specified accuracy indicators is established. The time delays in applying the methods examined have been determined experimentally. The limitations of the improved method and the prospects for further research are described. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.

Automated summary

The paper introduces an improved method for enhancing the accuracy of data ensemble predictions by improving two GRNNs ensemble and introducing additional SGTM structure to handle missing data management tasks in smart systems. Experimental results demonstrate the superior performance of this method in air condition monitoring dataset compared to methods of the same class.