UNSUPERVISED CLASSIFICATION BASED NEGATIVE SELECTION ALGORITHM
Résumé
In the last decade, artificial life has been considered as a promising area for rising challenges to unresolved computationalproblems. Inspired by natural phenomena, its study focuses on the exploration of complex systems. Neuronal networks, genetic
algorithms and more recently artificial immune systems are examples. Artificial Immune Systems (AIS) are one type of
intelligent algorithms inspired by the principles and processes of the human immune system. Emulating the discrimination
mechanism of the natural system, negative selection algorithm of AIS has been successfully applied on change and anomaly
detection.
This paper describes initial investigations in applying negative selection algorithm on pixel classification by maintaining a
population of detectors that remove undesired patterns. Its purpose is to find several detectors which do not match to self in the
population. We make use of an Euclidian space with an Euclidian performance measure on color images. The experimental
show promising results. The obtained classifier is effective and feasible.
Références
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Intelligence with mathematic 2OO1- 2002.
[3] De Castro, L. R. and Timmis, J. , "Artificial Immune
Systems: A New Computational Intelligence Paradigm",
Springer-Verlag New York, Inc., Secaucus, NJ, 2002
[4] Hart, E. Timmis, J., "Application areas of AIS: The past, the
present and the future", Applied Soft Computing, v.8 n.1,
p.191-201, January, 2008 .
[5] Dasgupta,D, "Artificial Immune Systems and their
applications", Springer-Verlag (Ed.), 1999.
[6] Timmis, J., "Artificial immune systems---today and
tomorrow", Natural Computing: an international journal, v.6
n.1, p.1-18, March 2007
[7] Forrest, S. and Al, "Self-Non self Discrimination in a
Computer", Proceedings of the IEEE Symposium on
Security and Privacy, p.202, May 16-18, 1994
[8] Ji,Z., Dasgupta, D., " Revisiting Negative Selection
Algorithms", Evolutionary Computation, Massachusetts
Institute of Technology v.15 n.2, p.223-251, 2007
[9] Igawa, K. and Ohashi, H., "A Discrimination Based
Artificial Immune System for Classification", Proceedings of
the International Conference on Computational Intelligence
for Modelling, Control and Automation and International
Conference on Intelligent Agents, Web Technologies and
Internet Commerce Vol-2 (CIMCA-IAWTIC'06), p.787-792,
November 28-30, 2005
[10] De Castro,L.N. and Von Zuben, F.J., " aiNet: an artificial
immune network for data analysis". In: Abbas, H.A.,
Charles, R.A.S., Newton, S. (Eds.), Data Mining: A
Heuristic Approach, Idea Group Publishing, USA. pp. 231-
259. 2002
[11] Tong, H., Zhao,M., and LiZ., "Applications of
Computational Intelligence in Remote Sensing Image
Analysis", (Eds.): ISICA 2009, CCIS 51, pp. 171–179,
Springer-Verlag Berlin Heidelberg 2009
[12] Cheng, J., and al, "Image Segmentation Based on Chaos
Immune Clone Selection Algorithm ", D.-S. Huang, L.
Heutte, and M. Loog (Eds.): ICIC 2007, LNAI 4682, pp.
505–512, 2007. Springer-Verlag Berlin Heidelberg 2007
[13] Xua, S., Wu, Y., «An algorithm for remote sensing image
classification based on artificial immune B cell network",
The International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Sciences. Vol. XXXVII.
Part B6b. Beijing 2008.
[14] McCoy, D.F. Devarajan, V., "Artificial immune systems and
aerial image segmentation", Proceedings of the IEEE
International Conference on Systems Conference on
Systems, Man, and Cybernetics: Computational Cybernetics
and Simulation, Orlando, FL, USA, pp. 867-872. 1997.
[15] LU, D., and WENG, Q., "A survey of image classification
methods and techniques for improving classification
performance", International Journal of Remote Sensing Vol.
28, No. 5, 10 , 823–870, March 2007.
[16] Ilea, D. and P. Whelan,"An adaptive unsupervised
classification algorithm based on color-texture coherence".
IEEE Transactions on Image Processing 17(10), 1926–1939.
2008
artificial life (special issue of al expert). Miller freeman.
September 1995.
[2] T. Carden, "Image processing with artificial life", Artificial
Intelligence with mathematic 2OO1- 2002.
[3] De Castro, L. R. and Timmis, J. , "Artificial Immune
Systems: A New Computational Intelligence Paradigm",
Springer-Verlag New York, Inc., Secaucus, NJ, 2002
[4] Hart, E. Timmis, J., "Application areas of AIS: The past, the
present and the future", Applied Soft Computing, v.8 n.1,
p.191-201, January, 2008 .
[5] Dasgupta,D, "Artificial Immune Systems and their
applications", Springer-Verlag (Ed.), 1999.
[6] Timmis, J., "Artificial immune systems---today and
tomorrow", Natural Computing: an international journal, v.6
n.1, p.1-18, March 2007
[7] Forrest, S. and Al, "Self-Non self Discrimination in a
Computer", Proceedings of the IEEE Symposium on
Security and Privacy, p.202, May 16-18, 1994
[8] Ji,Z., Dasgupta, D., " Revisiting Negative Selection
Algorithms", Evolutionary Computation, Massachusetts
Institute of Technology v.15 n.2, p.223-251, 2007
[9] Igawa, K. and Ohashi, H., "A Discrimination Based
Artificial Immune System for Classification", Proceedings of
the International Conference on Computational Intelligence
for Modelling, Control and Automation and International
Conference on Intelligent Agents, Web Technologies and
Internet Commerce Vol-2 (CIMCA-IAWTIC'06), p.787-792,
November 28-30, 2005
[10] De Castro,L.N. and Von Zuben, F.J., " aiNet: an artificial
immune network for data analysis". In: Abbas, H.A.,
Charles, R.A.S., Newton, S. (Eds.), Data Mining: A
Heuristic Approach, Idea Group Publishing, USA. pp. 231-
259. 2002
[11] Tong, H., Zhao,M., and LiZ., "Applications of
Computational Intelligence in Remote Sensing Image
Analysis", (Eds.): ISICA 2009, CCIS 51, pp. 171–179,
Springer-Verlag Berlin Heidelberg 2009
[12] Cheng, J., and al, "Image Segmentation Based on Chaos
Immune Clone Selection Algorithm ", D.-S. Huang, L.
Heutte, and M. Loog (Eds.): ICIC 2007, LNAI 4682, pp.
505–512, 2007. Springer-Verlag Berlin Heidelberg 2007
[13] Xua, S., Wu, Y., «An algorithm for remote sensing image
classification based on artificial immune B cell network",
The International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Sciences. Vol. XXXVII.
Part B6b. Beijing 2008.
[14] McCoy, D.F. Devarajan, V., "Artificial immune systems and
aerial image segmentation", Proceedings of the IEEE
International Conference on Systems Conference on
Systems, Man, and Cybernetics: Computational Cybernetics
and Simulation, Orlando, FL, USA, pp. 867-872. 1997.
[15] LU, D., and WENG, Q., "A survey of image classification
methods and techniques for improving classification
performance", International Journal of Remote Sensing Vol.
28, No. 5, 10 , 823–870, March 2007.
[16] Ilea, D. and P. Whelan,"An adaptive unsupervised
classification algorithm based on color-texture coherence".
IEEE Transactions on Image Processing 17(10), 1926–1939.
2008
Comment citer
BENDIAB, ESMA; K. KHOLLADI, M..
UNSUPERVISED CLASSIFICATION BASED NEGATIVE SELECTION ALGORITHM.
Courrier du Savoir, [S.l.], v. 14, mai 2014.
ISSN 1112-3338.
Disponible à l'adresse : >http://univ-biskra.dz/revues/index.php/cds/article/view/408>. Date de consultation : 22 déc. 2024
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