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.

Learning rule representations from data / B. Apolloni, A.A.F. Brega, D. Malchiodi, G. Palmas, A.M. Zanaboni. - In: IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS. - ISSN 1083-4427. - 36:5(2006 Sep), pp. 1010-1028.

Learning rule representations from data

B. Apolloni
Primo
;
A.A.F. Brega
Secondo
;
D. Malchiodi;A.M. Zanaboni
Ultimo
2006-09

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.
algorithmic inference; Boolean formula simplification; computational learning; fuzzy granular computing; mutual information; probably approximately correct (PAC) meditation; rough sets; rule learning; sentry points
Settore INF/01 - Informatica
Article (author)
File in questo prodotto:
File Dimensione Formato  
01678029.pdf

accesso riservato

Tipologia: Publisher's version/PDF
Dimensione 604.49 kB
Formato Adobe PDF
604.49 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/30051
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 16
  • ???jsp.display-item.citation.isi??? 15
social impact