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


Kancherla Jonah Nishanth, Vadlamani Ravi, Journal of Information Processing Systems Vol. 9, No. 4, pp. 633-650, Aug. 2013

10.3745/JIPS.2013.9.4.633
Keywords: Data Imputation, General Regression Neural Network (GRNN), Evolving Clustering Method (ECM), Imputation, K-Medoids clustering, k-Means Clustering, MLP
Fulltext:

Abstract

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 they 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 data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing vlaues with cluster centers, as part of the local learnig strategy Stage 2 refines the resultant approximate values using a Genearal 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 emploly 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 imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Kancherla Jonah Nishanth and Vadlamani Ravi (2013). A Computational Intelligence Based Online Data Imputation Method: An Application For Banking. Journal of Information Processing Systems, 9(4), 633-650. DOI: 10.3745/JIPS.2013.9.4.633.

[IEEE Style]
K. J. Nishanth and V. Ravi, "A Computational Intelligence Based Online Data Imputation Method: An Application For Banking," Journal of Information Processing Systems, vol. 9, no. 4, pp. 633-650, 2013. DOI: 10.3745/JIPS.2013.9.4.633.

[ACM Style]
Kancherla Jonah Nishanth and Vadlamani Ravi. 2013. A Computational Intelligence Based Online Data Imputation Method: An Application For Banking. Journal of Information Processing Systems, 9, 4, (2013), 633-650. DOI: 10.3745/JIPS.2013.9.4.633.