A Computational Intelligence Based Online Data Imputation Method: An Application For Banking

Abstract ² All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and the y also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real -time. The paper proposes a computational intelligence based architecture for online da ta imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation techn ique has 2 stages. In stage 1, E volving Clustering M ethod (ECM) is used to replace the missing values with cluster centers, as pa rt of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique . The offline imputation techniques employ K -Means or K -Medoids and Multi Layer Perceptron (MLP) or GRNN in Stage -1 and Stage -2 respectively. Several experiments were conducted on 8 benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imp utation techniques. In terms of Mean A bsolute P ercentage E rror (MAPE) , the results indicate that the difference between the proposed best offline imputation method viz., K -Medoids+GRNN and the proposed online imputati on method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, be ing less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation tech niques. This is the significant outcome of the study. Further more , GRNN in stage -2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets. Keywords ² Data Imputation, General R egression Neural Network (GRNN), Evolving C lustering M ethod (ECM), Imputation, K -Medoids clustering, K -Means clustering, MLP