Research on Classification Algorithm Based on Fractal Growth

Li Zhao; Wei Dong Li

doi:10.4028/www.scientific.net/AMR.461.757

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 461Research on Classification Algorithm Based on...

Research on Classification Algorithm Based on Fractal Growth

Abstract:

Pattern classification is a key technique in fields like artificial intelligence, computer vision, machine learning, etc.. To deal with this problem, this paper introduces a classifier based on fractal growth. A fractal growing algorithm using a cost calculating method is presented. Along with the fractal growing, a feature space is divided into several areas corresponding to different classes. And then, a classifier based on fractal growth has been realized. The detailed design is described in the paper. The classifier was tested with the circle-in-the-square problem and a high accuracy rate was achieved.

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Periodical:

Advanced Materials Research (Volume 461)

Pages:

757-762

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.461.757

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Online since:

February 2012

Authors:

Li Zhao, Wei Dong Li

Keywords:

Fractal Growth, Pattern Classification, Weighted Euclid Distance

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References

[1] Mandelbrot, B. B.: Fractals, Form, Chance and Dimension. San Francisco: W.H. Freeman and Co. (1977).

Google Scholar

[2] Mandelbrot, B. B.: The Fractal Geometry of Nature. San Francisco: W.H. Freeman and Co. (1982).

Google Scholar

[3] Quinlan, J. R. C4. 5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc. (1993).

Google Scholar

[4] L. Breiman, J. Friedman, R. Olshen, and C. Stone.: Classification and Regression Trees. Wadsworth, Belmont, CA, (1984).

Google Scholar

[5] Hastie, T. and Tibshirani, R.: Discriminant Adaptive Nearest Neighbor Classification. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI). 18, 6 (Jun. 1996), 607-616.

DOI: 10.1109/34.506411

Google Scholar

[6] Naive Bayes Hand, D.J., Yu, K.: Idiot's Bayes: Not So Stupid After All? Internat. Statist. Rev. 69, 385-398, (2001).

DOI: 10.1111/j.1751-5823.2001.tb00465.x

Google Scholar

[7] Shi Xueming, Wang Jiaying: Simulated Annealing Method[J]. Chinese Journal of Engineering Geophysics. 2007-03.

Google Scholar

[8] Lang, K.J. and Witbrock, M.J.: Learning to tell two spirals apart, in Touretzky, D., Hinton, G., and Sejnowski, T., eds., Procedings of the 1988 Connectionist Models Summer School, San Mateo, CA: Morgan Kaufmann. (1988).

DOI: 10.1145/1056754.1056760

Google Scholar

[9] Tian DaXin, Liu YanHeng, Li Bin, Wu Jing.: Distributed Neural Network Learning Algorithm Based on Hebb Rule, CHINESE JOURNAL OF COMPU TERS, Vol. 30 No. 8. Aug. (2007).

Google Scholar

[10] Brown, A.D., Card H.C.: Evolutionary artificial neural networks[J], Electrical and Computer Engineering, 1997. IEEE 1997 Canadian Conference on Electrical and Computer Engineering, vol. 1: 313 – 317.

DOI: 10.1109/ccece.1997.614852

Google Scholar