Evaluating the Impact of Missing Data Imputation through the use of the Random Forest Algorithm

@inproceedings{Pantanowitz2008EvaluatingTI,
  title={Evaluating the Impact of Missing Data Imputation through the use of the Random Forest Algorithm},
  author={Adam Pantanowitz and Tshilidzi Marwala},
  year={2008}
}
This paper presents an impact assessment for the imputation of missing data. The data set used is HIV Seroprevalence data from an antenatal clinic study survey performed in 2001. Data imputation is performed through five methods: Random Forests, Autoassociative Neural Networks with Genetic Algorithms, Autoassociative Neuro-Fuzzy configurations, and two Random Forest and Neural Network based hybrids. Results indicate that Random Forests are superior in imputing missing data in terms both of… CONTINUE READING

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Key Quantitative Results

  • Results indicate that Random Forests are superior in imputing missing data in terms both of accuracy and of computation time, with accuracy increases of up to 32% on average for certain variables when compared with autoassociative networks.