Two Improvements in Genetic Programming for Image Classification

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Yamin Li and Jinru Ma and Qiuxia Zhao: Two Improvements in Genetic Programming for Image Classification: 2008 IEEE World Congress on Computational Intelligence, pp. 2492-2497, IEEE Press, 1-6 June 2008.



A new classification algorithm for multi-image classification in genetic programming (GP) is introduced, which is the centered dynamic class boundary determination with quick-decreasing power value of arithmetic progression. In the classifier learning process using GP for multi-image classification, different sets of power values are tested to achieve a more suitable range of margin values for the improvement of the accuracy of the classifiers. In the second development, the program size is introduced into the fitness function to control the size of program growth during the evolutionary learning process. The approach is examined on a Chinese character image data set and a grass leaves data set, both of which have four or more classes. The experimental results show that while dealing with complicated problems of multi-image classification, the new approach can be used for more accurate classification and work better than the previous algorithms of either static or dynamic class boundary determination. With the fitness function, the size of the programs in the population can be controlled effectively and shortened considerably during evolution. Thus, the readability of the programs could be seemingly improved.

Extended Abstract


Used References

W. A. Tackett. Genetic generation of "dendritic" tree for image classification. In Proceedings of WCNN93, IEEE Press, July 1993: pp. 646-649.

W. A. Tackett. Genetic programming for feature discovery and image discrimination Proceeding of the 5th International Conference on Genetic Algorithm, ICGA-93, University of Illinois at Urbana-Champaign, 17-21, July 1993. Morgan Kaufmann. pp. 303-309

M. Zhang. A Domain Independent Approach to 2D Object Detection Based on the Neural and Genetic Paradigms. PhD thesis, RMIT University, 2000,

M. Zhang and V. Ciesielski. Genetic programming for multiple class object detection. Proceeding of the 12th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, December 1999, Springer-Verlag Berlin Heidelberg. Lecture Notes in Artificial Intelligence (LNAI Volume 1747) pp. 180-192.

T. Back and H. P. Schwefel. Evolutionary computation: an overview. In Proceedings of IEEE International conference on Evolutionary Computation, 1996, pp. 20-29.

M. Zhang and W. Smart Genetic programming with gradient descent search for multi-class object classification, In Maarten Keijzer, Una-May O'Rcilly, Simon M. Lucas, Ernesto Costa, and Terence Soulc, editors, Genetic Programming 7th European Conference, Euro GP 2004, Proceedings, Lecture Notes in Computer Science. Vol. 3003, Coimbra, Portugal, 5-7 April 2004. Springer-Verlag, pp. 399-408.

W. Smart and M. Zhang. Classification strategies for image classification in genetic programming, In Donald Bailey, editor, Proceeding of Image and Vision Computing Conference, Palmerston North, New Zealand, November 2003, pp. 402-407.

W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone, Genetic Programming: An Introduction on the Automatic Evolution of Computer Programs and Its Applications. SanFrancisco, Calif: Morgan Kaufmann Publishers; Heidelburg: Dpunkt-verlag, 1998. Subject: Genetic Programming (Computer Science); ISBN; 1955860-510-X.

John R. Koza, Genetic programming; on the programming of computers by means of natural selection. Cambridge, Mass.: MIT Press, London, England, 1992.

T. Loveard and V. Ciesielski. Representing classification problems in genetic programming. In Jong-Hwan Kim, editor, Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, South Korea, May 2001. pp. 1070-1077.

W. Smart, M. Zhang. Probability based genetic programming for multiclass object classification. Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence. Lecture Notes in Computer Science. Vol. 3157, Springer 2004. pp. 251-261.


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