Compute entropy to detect problematic predictors
Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing
Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling
Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising
Use Monte-Carlo permutation methods to assess the role of good luck in performance results
Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions
