Estimation of Missing Data Using Computational Intelligence and Decision Trees

Details Estimation of Missing Data Using Computational Intelligence and Decision Trees Estimation of Missing Data Using Computational Intelligence and Decision Trees

2007-09-11

arxiv.org/abs/0709.1640v1

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14 pages

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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 minimize an error function derived from the respective model. The models' 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 model's average accuracy increasing from 75.8% to 86.3% while that of the PCA-NN based model increasing from 66.1% to 81.6%.

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