Abstract

DC* is a method for generating interpretable fuzzy information granules from pre-classified data. It is based on the subsequent application of LVQ1 for data compression and an ad-hoc procedure based on A* to represent data with the minimum number of fuzzy information granules satisfying some interpretability constraints. While being efficient in tackling several problems, the A* procedure included in DC* may happen to require a long computation time because the A* algorithm has exponential time complexity in the worst case. In this paper, we approach the problem of driving the search process of A* by suggesting a close-to-optimal solution that is produced through a Genetic Algorithm (GA). Experimental evaluations show that, by driving the A* algorithm embodied in DC* with a GA solution, the time required to perform data granulation can be reduced from 45% to 99%.

Autore Pugliese

• Castiello Ciro , Mencar Corrado

Tutti gli autori

• CASTIELLO C.;MENCAR C.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2017

ISSN

1544-5615

ISBN

978-1-5090-6034-4; 978-1-5090-6033-7; 978-1-5090-6035-1

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

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Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

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