Distinguish score metrics (Section 2.1) and search algorithms (Section 2.2). A local score based structure learning can be selected by choosing one in the weka.classifiers.bayes.net.search.local package.
Local score based algorithms have the following options in common:
initAsNaiveBayes if set true (default), the initial network structure used for starting the traversal of the search space is a naive Bayes network structure. That is, a structure with arrows from the class variable to each of the attribute variables.
If set false, an empty network structure will be used (i.e., no arrows at all).
markovBlanketClassifier (false by default) if set true, at the end of the traversal of the search space, a heuristic is used to ensure each of the attributes are in the Markov blanket of the classifier node. If a node is already in the Markov blanket (i.e., is a parent, child of sibling of the classifier node) nothing happens, otherwise an arrow is added.
If set to false no such arrows are added.
scoreType determines the score metric used (see Section 2.1 for details). Currently, K2, BDe, AIC, Entropy and MDL are implemented.
maxNrOfParents is an upper bound on the number of parents of each of the nodes in the network structure learned.