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IOS Press Theory and Application of Cellular Automata For Pattern Classification
BibTeX
@MISC{Maji_iospress,
author = {Pradipta Maji and Rama Shaw and Niloy Ganguly and Biplab K. Sikdar and P. Pal Chaudhuri},
title = {IOS Press Theory and Application of Cellular Automata For Pattern Classification},
year = {}
}
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Abstract
Abstract. This paper presents the theory and application of a high speed, low cost pattern classifier. The proposed classifier is built around a special class of sparse network referred to as Cellular Automata (CA). A specific class of CA, termed as Multiple Attractor Cellular Automata (MACA), has been evolved through Genetic Algorithm (GA) formulation to perform the task of pattern classification. The versatility of the classification scheme is illustrated through its application in three diverse fields- data mining, image compression, and fault diagnosis. Extensive experimental results demonstrate better performance of the proposed scheme over popular classification algorithms in respect of memory overhead and retrieval time with comparable classification accuracy. Hardware architecture of the proposed classifier has been also reported.
Keyphrases
pattern classification cellular automaton io press theory classification scheme image compression fault diagnosis hardware architecture memory overhead multiple attractor cellular automaton genetic algorithm retrieval time diverse field data mining specific class special class popular classification algorithm comparable classification accuracy low cost pattern classifier high speed sparse network extensive experimental result
