See: UCI Machine Learning datasets for R

• D1 - bank-marketing
• D2 - census-income
• D3 - credit-card
• D4 - magic
• D5 - wine-quality

See: caret

• K1 - glm
• K2 - nnet
• K3 - svmLinear
• K4 - rpart
• K5 - nnet

• I1 - median & mode
• I2 - random forest (missForest)
• I3 - chained equations (mice)

• Ai - aggregation strategies from the article Solving the problem of incomplete data in medical diagnosis via interval modeling

For each Di:

1. Divide Di into Di^1 and Di^2
2. Learn classifiers Kj on Di^1 (10-CV)
3. Du^2 := randomly obscured Di^2 with data loss from 0% to 50%
4. Calculate accuracy, sensitivity, specificity and decisiveness for classifiers Kj on Di^2
5. Choose the best impuation on Di^2
6. Find aggregation operator:
   1. Calculate inteval predictions on Di^2
   2. Choose the best aggregation operator on inteval predictions
7. Compare Kj with the best imputation and agregation operator