Editing Feature selection (section)

==Structure learning==
Filter feature selection is a specific case of a more general paradigm called [[Structured prediction|structure learning]]. Feature selection finds the relevant feature set for a specific target variable whereas structure learning finds the relationships between all the variables, usually by expressing these relationships as a graph. The most common structure learning algorithms assume the data is generated by a [[Bayesian Network]], and so the structure is a [[Directed graph|directed]] [[graphical model]]. The optimal solution to the filter feature selection problem is the [[Markov blanket]] of the target node, and in a Bayesian Network, there is a unique Markov Blanket for each node.<ref>{{cite journal|last1=Aliferis|first1=Constantin|title=Local causal and markov blanket induction for causal discovery and feature selection for classification part I: Algorithms and empirical evaluation|journal=Journal of Machine Learning Research|date=2010|volume=11|pages=171–234|url=http://jmlr.org/papers/volume11/aliferis10a/aliferis10a.pdf}}</ref>