RBFFCA: A Hybrid Pattern Classifier Using Radial Basis Function and Fuzzy Cellular Automata

Issue title: Special issue on DLT'04

Article type: Research Article

Authors: Maji, Pradipta | Chaudhuri, P. Pal

Affiliations: Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700 108, India. E-mail: pmaji@isical.ac.in | Cellular Automata Research Laboratory (CARL), Techno India Campus, EM 4/1, Sector V, Kolkata, 700 091, India. E-mail: palchau@carltig.res.in

Abstract: A hybrid learning algorithm, termed as RBFFCA, for the solution of classification problems with real valued inputs is proposed. It comprises an integration of the principles of radial basis function (RBF) and fuzzy cellular automata (FCA). The FCA has been evolved through genetic algorithm (GA) formulation to perform pattern classification task. The versatility of the proposed hybrid scheme is illustrated through its application in diverse fields. Simulation results conducted on benchmark database show that the hybrid pattern classifier achieves excellent performance both in terms of classification accuracy and learning efficiency. Extensive experimental results supported with analytical formulation establish the effectiveness of RBFFCA based pattern classifier and prove it as an efficient and cost-effective alternative for the classification problem.

Keywords: Cellular Automata, Fuzzy Cellular Automata, Radial Basis Function, Genetic Algorithm, Pattern Classification

Journal: Fundamenta Informaticae, vol. 78, no. 3, pp. 369-396, 2007