Cellular Automata Evolution for Pattern Classification
Pradipta Maji2004, Lecture Notes in Computer Science
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Abstract
This paper presents the design and application of a treestructured pattern classifier, built around a special class of linear Cellular Automata (CA), termed as Multiple Attractor CA (MACA). Since any non-trivial classification function is non-linear in nature, the principle of realizing the non-linear function with multiple (piece-wise) linear functions is employed. Multiple (linear) MACAs are utilized to address the classification of benchmark data used to evaluate the performance of a classifier. Extensive experimental results have established the potential of MACA based tree-structured pattern classifier. Excellent classification accuracy with low memory overhead and low retrieval time prove the superiority of the proposed pattern classifier over conventional algorithms.
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