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Keywords:

  • partial correlation;
  • clustering;
  • classification;
  • regression;
  • knowledge extraction;
  • generalized topological overlap measure;
  • linear discriminant analysis;
  • partial least squares;
  • ordinary least squares

Current supervised approaches, such as classification and regression methodologies, are strongly focused on optimizing estimation accuracy metrics, leaving the interpretation of the results produced as a secondary concern. However, in the analysis of complex systems, one of the main interests is precisely the induction of relevant associations, to understand or clarify the way the system operates. Two related frameworks for addressing supervised learning problems (classification and regression) are presented, that incorporate interpretational-oriented analysis features right from the onset of the analysis. These features constrain the predictive space, in order to introduce interpretable elements in the final model. Interestingly, such constraints do not usually compromise the methods' performance, when compared to their unconstrained versions. The frameworks, called network-induced classification (NI-C), and network-induced regression (NI-R), share a common methodological backbone, and are described in detail, as well as applied to real-world case studies. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1570–1587, 2013