Affiliations: Department of Computer Science and Technology, Bengal
Engineering College (D U), Howrah, India 711103 | Technical University of Dresden, High Performance
Computing Centre, Zellescher Weg 12, Willers-Bau A 115, D-01069 Dresden, Germany
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.
