Data imputation via evolutionary computation, clustering and a neural network

In this paper, two novel hybrid imputation methods involving particle swarm optimization (PSO), evolving clustering method (ECM) and autoassociative extreme learning machine (AAELM) in tandem are proposed, which also preserve the covariance structure of the data. Further, we removed the randomness of AAELM by invoking ECM between input and hidden layers. Moreover, we selected the optimal value of Dthr using PSO, which simultaneously minimizes two error functions viz., (i) mean squared error between the covariance matrix of the set of complete records and that of the set of total records, including imputed ones and (ii) absolute difference between the determinants of the two covariance matrices. The proposed methods outperformed many existing imputation methods in majority of the datasets. Finally, we also performed a statistical significance testing to ensure the credibility of our obtained results. Superior performance of one of the hybrids is attributed to the power of hybrid of local learning, global optimization and global learning. Both methods resolved a nagging issue of the difficult choice of Dthr value and its dominant influence on the results in ECM based imputation. We conclude that the proposed models can be used as a viable alternative to the existing ones for the data imputation.