Computational intelligence and decision trees for missing data estimation

This paper introduces a novel paradigm to impute missing data that combines a decision tree with an auto-associative neural network (AANN) based model and a principal component analysis-neural network (PCA-NN) based model. For each model, the decision tree is used to predict search bounds for a genetic algorithm that minimise an error function derived from the respective model. The modelspsila ability to impute missing data is tested and compared using HIV sero-prevalance data. Results indicate an average increase in accuracy of 13% with the AANN based modelpsilas average accuracy increasing from 75.8% to 86.3% while that of the PCA-NN based model increasing from 66.1 % to 81.6%.