Further evaluation of pruning in learning boolean functions

Author

Nicoletti, Maria Do Carmo ; Ramer, Arthur ; Monard, Maria Carolina

Author_Institution

Univ. Fed. de Sao Carlos, Brazil

Volume

2

fYear

2001

fDate

25-28 July 2001

Firstpage

956

Abstract

This work continues an earlier analysis (Castineira and Monard, 1990; Nicoletti and Monard, 1993) of the problem of pruning, within a framework of automated feature construction when learning boolean functions. Automated feature construction is implemented through three different biases, namely root, fringe and root-fringe. It presents an empirical evaluation of two pruning techniques (reduced error pruning and of its variation) based on their application to trees generated through an automated feature construction. These techniques, although at first studied only for classical boolean functions, appear very promising for an analysis of fuzzy boolean connectives

Keywords

Boolean functions; decision trees; fuzzy logic; learning by example; automated feature construction; boolean functions; constructive induction; decision trees; fringe bias; fuzzy boolean connectives; inductive learning; learning; learning systems; reduced error pruning; root bias; root-fringe bias; Art; Australia; Boolean functions; Classification tree analysis; Constraint theory; Decision trees; Error analysis; Induction generators; Iterative algorithms; Learning systems;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-7078-3

Type

conf

DOI

10.1109/NAFIPS.2001.944734

Filename

944734