Abstract

In zoning-based classification, a membership function defines the way a feature influences the different zones of the zoning method. This paper presents a new class of membership functions, named Fuzzy Membership Functions (FMFs), for zoning-based classification. These FMFs can be easily adapted to the specific characteristics of a classification problem in order to maximize classification performance. In this study, a real-coded genetic algorithm is presented to find, in a single optimization procedure, the optimal FMF together with the optimal zoning described by Voronoi Tessellation. The experimental results, carried out in the field of handwritten digit and character recognition, indicate that optimal FMF performs better than other membership functions based on abstract-level, ranked-level and measurement-level weighting models, which can be found in the literature.

Autore Pugliese

• Impedovo Donato , Pirlo Giuseppe

Tutti gli autori

• IMPEDOVO D.;PIRLO G.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2011

ISSN

1063-6706

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

26

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile