[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9717/kmms.2015.18.11.1281

Generation of Pattern Classifiers Based on Linear Nongroup CA

Choi, Un-Sook (Dept. of Information & Communications Eng., Tongmyong University)
Cho, Sung-Jin (Dept. of Applied Math., Pukyong National University)
Kim, Han-Doo (Institute of Basic Science and Dept. of Applied Math., Inje University)

Publication Information

Journal of Korea Multimedia Society / v.18, no.11, 2015 , pp. 1281-1288 More about this Journal

Abstract

Nongroup Cellular Automata(CA) having two trees in the state transition diagram of a CA is suitable for pattern classifier which divides pattern set into two classes. Maji et al. [1] classified patterns by using multiple attractor cellular automata as a pattern classifier with dependency vector. In this paper we propose a method of generation of a pattern classifier using feature vector which is the extension of dependency vector. In addition, we propose methods for finding nonreachable states in the 0-tree of the state transition diagram of TPMACA corresponding to the given feature vector for the analysis of the state transition behavior of the generated pattern classifier.

Keywords

Pattern Classifier; Cellular Automata(CA); Multiple Attractor CA(MACA); Nonreachable state; Feature Vector;

Citations & Related Records

Reference

1	J.V. Neumann, The Theory of Self-reproducing Automata, University of Illinois Press, Urban and London, 1966.
2	P. Maji, C. Shaw, N. Ganguly, B. Sikdar, and P.P. Chaudhuri, "Theory and Application of Cellular Automata for Pattern Classification," Fundamenta Informaticae, IOS Press, Vol. 58, No. 3-4, pp. 321- 354, 2003.
3	R.D. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, Wiley-Interscience, USA, 2001.
4	S.J. Cho, U.S. Choi, and H.D. Kim, "Analysis of Complemented CA Derived from a Linear TPMACA," Computers and Mathematics with Applications, Vol. 45, No. 4-5, pp. 680-698, 2003. DOI
5	P. Dasgupta, P.P Chattopadhyay, P.P. Chaudhuri, and I. Sengupta, "Cellular Automata-based Recursive Pseudoexhaustive Test Pattern Generator," IEEE Transaction on Computers, Vol. 50, No. 2, pp. 177-185, 2001. DOI
6	A.K. Das and P.P. Chaudhuri, "Vector Space Theoretic Analysis of Additive Cellular Automata and Its Application for Pseudo-exhaustive Test Pattern Generation," IEEE Transaction on Computers, Vol. 42, No. 2, pp. 177-185, 2001.
7	S. Bandini, L. Vanneschi, A. Wuensche, and A.B. Shehata, "Cellular Automata Pattern Recognition and Rule Evolution through a Neuro-genetic approach," Journal of Cellular Automata, Vol. 4, No. 3, pp. 171-181, 2009.
8	S.J. Cho, U.S. Choi, H.D. Kim, Y.H. Hwang, J.G. Kim, and S.H. Heo, "New Synthesis of One-dimensional 90/150 Linear Hybrid Group Cellular Automata," IEEE Transaction on Computer-Aided Design of Integrated Circuits Systems, Vol. 26, No. 9, pp. 1720-1724, 2007. DOI
9	J.S. Lee, H.H. Cho, K.H. Rhee, “Cellular Automata and it Applications,“ Journal of Korea Multimedia Society, Vol. 6, No. 4, pp. 610-619, 2003.
10	N. Ganguly, Cellular Automata Evolution : Theory and Applications in Pattern Recognition and Classification, Doctor's Thesis of CST Department BECDU India, 2003.
11	P.P. Chaudhuri, D.R. Chowdhury, S. Nandy, and C. Chattopadhyay, Additive Cellular Automata Theory and Applications, IEEE Computer Society Press, California, 1997.
12	J. Ponkaew, S. Wongthanavasu, and C. Lursinsap, "A Nonlinear Classifier using an Evolution of Cellular Automata," Proceeding of International Symposium on Intelligent Signal Processing and Communications Systems, pp. 1-5, 2011.
13	S.J. Cho, U.S. Choi, H.D. Kim, Y.H. Hwang, and J.G. Kim, "Analysis of 90/150 Two Predecessor Nongroup Cellular Automata," Lecture Notes in Computer Science, Vol. 5191, pp. 128-135, 2008.
14	S.J. Cho, H.D. Kim, U.S. Choi, S.T. Kim, J.G. Kim, S.H. Kwon, et al., "Generation of TPMACA for Pattern Classification," Lecture Notes in Computer Science, Vol. 8751, pp. 408-416, 2014.