A new online data imputation method based on general regression auto associative neural network

作者：Ravi Vadlamani^*; Krishna Mannepalli

来源：Neurocomputing, 2014, 138: 106-113.

DOI：10.1016/j.neucom.2014.02.037

• Data imputation
• Auto associative neural network
• General regression auto associative neural network
• Radial basis function auto associative neural network
• Particle swarm optimization

In this paper we proposed online, offline and semi-online data imputation models based on the four auto associative neural networks. The online model employs mean imputation followed by general regression auto associative neural network (GRAANN). The offline methods include mean imputation followed by particle swarm optimization based auto associative neural network (PSOAANN); mean imputation followed by particle swarm optimization based auto associative wavelet neural network (PSOAAWNN) and the semi-online method involving mean imputation followed by radial basis function auto associative neural network (RBFAANN). We compared the performance of these hybrid models with that of mean imputation and a hybrid imputation method viz, K-means and multi-layer perceptron (MLP) of Ankaiah and Ravi (2011) [65]. We tested the effectiveness of these models on four benchmark classification and four benchmark regression datasets; three bankruptcy prediction datasets and one credit scoring datasets under 10-fold cross-validation testing. From the experiments, we observed that the GRAANN yielded better imputation for the missing values than the rest of the models. We confirmed this by performing the Wilcoxon signed rank test to test the statistical significance between the methods proposed. It turned out that GRAANN outperformed other models in most of the datasets.

• 出版日期2014-8-22