Abstract
Current artificial immune system (AIS) classifiers have two major problems: (1) their populations of B-cells can grow to huge proportions and (2) optimizing one B-cell (part of the classifier) at a time does not necessarily guarantee that the B-cell pool (the whole classifier) will be optimized. In this paper, we describe the design of a new AIS algorithm and classifier system called simple AIS (SAIS). It is different from traditional AIS classifiers in that it takes only one B-cell, instead of a B-cell pool, to represent the classifier. This approach ensures global optimization of the whole system and in addition no population control mechanism is needed. We have tested our classifier on four benchmark datasets using different classification techniques and found it to be very competitive when compared to other classifiers.
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References
Zhang, G.P.: Neural networks for classification: A survey. Proc. IEEE Transactions on Systems, Man, and Cybernetics - Part C: / Applications and Reviews 30, 451–462 (2000)
Desai, V.S., Convay, D.G., Crook, J.N., Overstreet, G.A.: Credit scoring models in the credit union environment using neural networks and genetic algorithms. IMA Journal of Mathematics Applied in Business and Industry 8, 323–346 (1997)
Malhotra, R., Malhotra, D.K.: Evaluating consumer loans using neural networks. The International Journal of Management Science 31, 83–96 (2003)
Tarakanov, A., Dasgupta, D.: A formal model of an artificial immune system. BioSystems 55, 155–158 (2000)
Hunt, J.E., Cooke, D.E.: An adaptive, distributed learning system based on the immune system. In: Proc. IEEE International Conference on Systems, Man and Cybernetics, pp. 2494–2499 (1995)
Dasgupta, D., Forrest, S.: Novelty detection in time series data using ideas from immunology. In: Proc. ICSA 5th International Conference on Intelligent Systems, Reno, Nevada, pp. 87–92 (1996)
Timmis, J., Neal, M., Hunt, J.: An artificial immune system for data analysis. BioSystems 55, 143–150 (2000)
Watkins, A.: AIRS: A Resource Limited Artificial Immune Classifier. Master’s thesis, Mississippi State University (2001), available at http://www.cse.msstate.edu/~andrew/research/publications.html
Neal, M.: An artificial immune system for continuous analysis of time-varying data. In: Proc. First International Conference on Artificial Immune Systems (ICARIS), Canterbury, UK, pp. 76–85 (2002)
Engin, O., Doyen, A.: A new approach to solve hybrid shop scheduling problems by an artificial immune system. Future Generation Computer Systems 20, 1083–1095 (2004)
Nasraoui, O., Dasgupta, D., González, F.: A novel artificial immune system approach to robust data mining. In: Proc. Genetic and Evolutionary Computation Conference (GECCO), New York, pp. 356–363 (2002)
Timmis, J., Knight, T.: Artificial Immune Systems: Using the Immune System as Inspiration for Data Mining. In: Data Mining: A Heuristic Approach, pp. 209–230. Idea Group Publishing (2002)
Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaptation and machine learning. Physica D 20, 187–204 (1986)
Timmis, J., Neal, M.: A resource limited artificial immune system for data analysis. Knowledge-Based Systems 14, 121–130 (2001)
Watkins, A., Timmis, J., Boggess, L.: Artificial immune recognition system (AIRS): An immune-inspired supervised learning algorithm. Genetic Programming and Evolvable Machines 5, 291–317 (2004)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
Wilson, D.R., Martinez, T.R.: Improved heterogeneous distance functions. Journal of Artificial Intelligence Research 6, 1–34 (1997)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Duch, W.: Datasets used for Classification: Comparison of Results (2000), http://www.phys.uni.torun.pl/kmk/projects/datasets.html
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Leung, K., Cheong, F. (2006). A Simple Artificial Immune System (SAIS) for Generating Classifier Systems. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_19
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DOI: https://doi.org/10.1007/11941439_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
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