Learning rule representations from data

We discuss a procedure which extracts statistical and entropic information from data in order to discover Boolean rules underlying them. We work within a granular computing framework where logical implications between statistics on the observed sample and properties on the whole data population are stressed in terms of both probabilistic and possibilistic measures of the inferred rules. With the main constraint that the class of rules is not known in advance, we split the building of the hypotheses on them in various levels of increasing description complexity, balancing the feasibility of the learning procedure with the understandability and reliability of the formulas that are discovered. We appreciate the entire learning system in terms of truth tables, formula lengths, and computational resources through a set of case studies.