Abstract
This paper presents a new artificial immune network model that addresses the problem of non-parametric density estimation. The model combines immune ideas with the known Parzen window estimator. The model uses a general representation of antibodies, which leads to redefine the network dynamics. The model is able to perform on-line learning, that is to say, training samples are presented only once. Results from exploratory experiments are presented in order to give insights on the reliability of the estimations of the proposed model.
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References
Alpaydin, E.: Introduction to Machine Learning. The MIT Press, Cambridge (2004)
Girolami, M., He, C.: Probability density estimation from optimally condensed data samples. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1253–1264 (2003)
Stibor, T., Timmis, J.: An investigation into the compression quality of aiNET. In: Foundations of Computational Intelligence. IEEE Symposium on Computational Intelligence, HI, USA, pp. 495–502 (2007)
Alonso, O., González, F.A., Niño, F., Galeano-Huertas, J.C.: A solution concept for artificial immune networks: A coevolutionary perspective. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 35–46. Springer, Heidelberg (2007)
Deng, Z., Chung, F.L., Wang, S.: FRSDE: Fast reduced set density estimator using minimal enclosing ball approximation. Pattern Recognition 41, 1363–1372 (2008)
de Castro, L.N., Zuben, F.J.V.: aiNet: An Artificial Immune Network for Data Analysis. In: Abbas, H.A., Sarker, R.A., Newton, C.S. (eds.) Data Mining: A Heuristic Approach, pp. 231–259. Idea Group Publishing, USA (2001)
Nasraoui, O., Cardona, C., Rojas, C., González, F.: TECNO-STREAMS: Tracking evolving clusters in noisy data streams with a scalable immune system learning model. In: Third IEEE International Conference on Data Mining, Melbourne, FL. IEEE, Los Alamitos (2003)
Nasraoui, O., González, F., Dasgupta, D.: The Fuzzy Artificial Immune System: Motivations, Basic Concepts and Application to Clustering and Web Profiling. In: IEEE International Conference on Fuzzy Systems, Hawaii, HI, pp. 711–716. IEEE, Los Alamitos (2002)
Neal, M.: An Artificial Immune System for Continuous Analysis of Time-Varying Data. In: Timmis, J., Bentley, P.J. (eds.) Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS), vol. 1, pp. 76–85. University of Kent at Canterbury, University of Kent at Canterbury Printing Unit (2002)
Neal, M.: Meta-Stable Memory in an Artificial Immune Network. In: Timmis, J., Bentley, P., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 168–180. Springer, Heidelberg (2003)
Timmis, J., Neal, M.: A Resource Limited Artificial Immune System for Data Analysis. Knowledge-Based Systems 14, 121–130 (2001)
Timmis, J., Neal, M., Hunt, J.: An Artificial Immune System for Data Analysis. BioSystems 55, 143–150 (2000)
de Castro, L.N., Timmis, J.: An Artificial Immune Network for Multimodal Optimisation. In: Congress on Evolutionary Computation. Part of the 2002 IEEE World Congress on Computational Intelligence, Honolulu, Hawaii, USA, pp. 699–704. IEEE, Los Alamitos (2002)
Ishiguro, A., Ichikawa, S., Uchikawa, Y.: A Gait Acquisition of Six-Legged Robot Using Immune Networks. In: Proceedings of International Conference on Intelligent Robotics and Systems (IROS 1994), Munich, Germany, vol. 2, pp. 1034–1041 (1994)
Luh, G.C., Liu, W.W.: Reactive Immune Network Based Mobile Robot Navigation. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) Proceeding of the Third Conference ICARIS, pp. 119–132. Springer, Heidelberg (2004)
Michelan, R., Zuben, F.J.V.: Decentralized Control System for Autonomous Navigation Based on an Evolved Artificial Immune Network. In: Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu, HI, vol. 2, pp. 1021–1026. IEEE, Los Alamitos (2002)
Vargas, P.A., de Castro, L.N., Michelan, R., Zuben, F.J.V.: An Immune Learning Classifier System for Autonomous Navigation. In: Timmis, J., Bentley, P., Hart, E. (eds.) Proceedings of the Second International Conference ICARIS, Edinburg, UK, pp. 69–80. Springer, Heidelberg (2003)
Galeano, J.C., Veloza-Suan, A., González, F.A.: A comparative analysis of artificial immune network models. In: Beyer, H. (ed.) Proceedings of the Genetic and Evolutionary Computation Conference, pp. 361–368. ACM Press, New York (2005)
Perelson, A.S., Weisbuch, G.: Immunology for Physicists. Reviews of Modern Physics 69, 1219–1267 (1997)
de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
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Galeano-Huertas, J.C., González, F.A. (2008). INDIE: An Artificial Immune Network for On-Line Density Estimation. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_24
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DOI: https://doi.org/10.1007/978-3-540-88636-5_24
Publisher Name: Springer, Berlin, Heidelberg
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