PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning rule representations from data
Bruno Apolloni, Andrea Brega, Dario Malchiodi, Giorgio Palmas and Anna Maria Zanaboni
IEEE Transactions on System, Man and Cybernetics, Part A 2005.

Abstract

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1310
Deposited By:Dario Malchiodi
Deposited On:28 November 2005