Abstract
This article proposes a new classifier inspired on a biological immune systems’ characteristic. This immune based predictor also belongs to the class of k-nearest-neighbors algorithms. Nevertheless, its main features, compared to other artificial immune classifiers, are the assumption that training set is the antibodies’ population and a suppression mechanism that tries to reduce the training set into a smaller subset. This subset is supposed to contain the most significative samples, without loosing much capability of generalization. It is known that in prediction problems, the choice of a good training set is crucial for the classification process. And this is the focus of this research. Experiments using some benchmarks and the analysis of the results of our ongoing work are presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gordon, A.: Classification. Chapman and Hall, London (1981)
Espínola, R.P., Ebecken, N.F.F.: On extending f-measure and g-mean metrics to multi-class problems. DATA MINING VI - Data Mining, Text Mining and Their Business Applications 1, 25–34 (2005)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Watkins, A.: AIRS: A resource limited immune classifier. Master’s thesis, Mississipi State University (2001)
Watkins, A.B., Boggess, L.C.: A resource limited artificial immune classifier. In: Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Honolulu, HI, USA, pp. 926–931. IEEE Computer Society Press, Los Alamitos (2002)
Watkins, A.B., Boggess, L.C.: A new classifier based on resource limited artificial immune systems. In: Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Honolulu, HI, USA, pp. 1546–1551. IEEE Computer Society Press, Los Alamitos (2002)
Watkins, A., Timmis, J.: Artificial immune recognition system AIRS: Revisions and refinements. In: Timmis, J., Bentley, P. (eds.) ICARIS 2002. 1st Intl. Conference on Artificial Immune Systems, pp. 173–181. University of Kent (2002)
Goodman, D., Boggess, L., Watkins, A.: An investigation into the source of power for airs, an artificial immune classification system. In: IJCNN 2003. Intl. Joint Conference on Neural Networks, Portland, OR, USA, pp. 1678–1683 (2003)
Watkins, A., Timmis, J., Boggess, L.: Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm, vol. 5, pp. 291–317. Springer, Netherlands (2004)
Watkins, A., Timmis, J.: Exploiting parallelism inherent in AIRS, an artificial immune classifier. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 427–438. Springer, Heidelberg (2004)
Watkins, A.: Exploiting Immunological Metaphors in the Development of Serial, Parallel, and Distributed Learning Algorithms. PhD thesis, University of Kent, Canterbury, UK (2005)
Marwah, G., Boggess, L.: Artificial immune systems for classification: Some issues (2002)
Carter, J.H.: The immune system as a model for pattern recognition and classification. Journal of the American Medical Informatics Association 7, 28–41 (2000)
Timmis, J., Neal, M., Hunt, J.: An artificial immune system for data analysis. Biosystems 55, 143–150 (2000)
Timmis, J., Neal, M.: A resource limited artificial immune system for data analysis. Knowledge Based Systems 14, 121–130 (2001)
de Castro, L.N., Zuben, F.J.V.: Ainet: An artificial immune network for data analysis. In: Hussein, A., Abbass, R.A.S., Newton, C.S. (eds.) Data Mining: A Heuristic Approach, Idea Group Publishing, USA (2001)
Timmis, J., Neal, M.J.: A Resource Limited Artificial Immune System for Data Analysis. In: Timmis, J., Neal, M.J. (eds.) Research and Development in Intelligent Systems XVII. Proceedings of ES 2000, Cambridge, UK, pp. 19–32 (2000)
Knight, T., Timmis, J.: Aine: An immunological approach to data mining. In: Cercone, N., Lin, T., Wu, X. (eds.) IEEE International Conference on Data Mining, San Jose, CA, USA, pp. 297–304. IEEE, New York (2001)
Timmis, J., Neal, M.: A resource limited artificial immune system for data analysis. Knowledge Based Systems 14, 121–130 (2001)
Knight, T., Timmis, J.: Assessing the performance of the resource limited artificial immune system AINE. Technical Report 3-01, Canterbury, Kent. CT2 7NF (2001)
Timmis, J., Knight, T.: Artificial immune systems: Using the immune system as inspiration for data mining. In: Abbass, H.A., Sarker, R.A., Newton, C.S. (eds.) Data Mining: A Heuristic Approach, pp. 209–230. Group Idea Publishing (2001)
de Castro, L.N.: Immune Engineering: Development of Computational Tools Inspired by the Artificial Immune Systems (in Portuguese). PhD thesis, DCA FEEC/UNICAMP, Campinas/SP, Brazil (August 1998 to May 2001)
de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach, 1st edn. vol. 1 (2002)
Janeway, C.A., Travers, P., Walport, M., Shlomchik, M.: Immunobiologia: O sistema imune na saúde e na doença (in Portuguese), 5th edn. Artes Médicas (2001)
Perelson, A.S., Oster, G.F.: The shape space model. Journal of Theoretical Biology 81, 645–670 (1979)
Perelson, A.S., Weisbuch, G.: Immunology for physicists. Reviews of Modern Physics 69, 1219 (1997)
Voronoi, G.: Nouvelles applications des paramétres continus á la théorie des formes quadratiques. J. für die Reine und Angewandte Mathematik 133, 97–178 (1907)
Newman, D.J., Hettich, S., Merz, C.B.C.: UCI repository of machine learning databases (1998)
Ross, T.J.: Fuzzy Logic with Engineering Applications. McGraw Hill, New York (1995)
Cristianini, N., Shawe-Taylor, J.: An Introduction to support vector machines (and other kernel-based learning methods). Cambridge University Press, Cambridge (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Figueredo, G.P., Ebecken, N.F.F., Barbosa, H.J.C. (2007). The SUPRAIC Algorithm: A Suppression Immune Based Mechanism to Find a Representative Training Set in Data Classification Tasks. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds) Artificial Immune Systems. ICARIS 2007. Lecture Notes in Computer Science, vol 4628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73922-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-73922-7_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73921-0
Online ISBN: 978-3-540-73922-7
eBook Packages: Computer ScienceComputer Science (R0)