Global score metric based structure learning

Common options for cross-validation based algorithms are:
initAsNaiveBayes, markovBlanketClassifier and maxNrOfParents (see Section 2 for description).

Further, for each of the cross-validation based algorithms the CVType can be chosen out of the following:

• Leave one out cross-validation (loo-cv) selects $m=N$ training sets simply by taking the data set $D$ and removing the $i$th record for training set $D_i^t$. The validation set consist of just the $i$th single record. Loo-cv does not always produce accurate performance estimates.

• K-fold cross-validation (k-fold cv) splits the data $D$ in $m$ approximately equal parts $D_1,\ldots,D_m$. Training set $D_i^t$ is obtained by removing part $D_i$ from $D$. Typical values for $m$ are 5, 10 and 20. With $m=N$, k-fold cross-validation becomes loo-cv.

• Cumulative cross-validation (cumulative cv) starts with an empty data set and adds instances item by item from $D$. After each time an item is added the next item to be added is classified using the then current state of the Bayes network.

Finally, the useProb flag indicates whether the accuracy of the classifier should be estimated using the zero-one loss (if set to false) or using the estimated probability of the class.

The following search algorithms are implemented: K2, HillClimbing, RepeatedHillClimber, TAN, Tabu Search, Simulated Annealing and Genetic Search. See Section 2 for a description of the specific options for those algorithms.