Missing values: how many can they be to preserve classification reliability?

  title={Missing values: how many can they be to preserve classification reliability?},
  author={Martti Juhola and Jorma Laurikkala},
  journal={Artificial Intelligence Review},
Using five medical datasets we detected the influence of missing values on true positive rates and classification accuracy. We randomly marked more and more values as missing and tested their effects on classification accuracy. The classifications were performed with nearest neighbour searching when none, 10, 20, 30% or more values were missing. We also used discriminant analysis and naïve Bayesian method for the classification. We discovered that for a two-class dataset, despite as high as 20… CONTINUE READING
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