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Warianty tytułu
Języki publikacji
Abstrakty
Artificial immune systems (AIS) have become popular among researchers and have been applied to a variety of tasks. Developing supervised learning algorithms based on metaphors from the immune system is still an area in which there is much to explore. In this paper a novel supervised immune algorithm based on clonal selection framework is proposed. It evolves a population of linear classifiers used to construct a set of classification rules. Aggregating strategies, such as bagging and boosting, are shown to work well with the proposed algorithm as the base classifier.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
325--342
Opis fizyczny
Bibliogr. 23 poz., wykr.
Twórcy
autor
autor
- Institute of Computer Science, Cracow University of Technology, Cracow, Poland
Bibliografia
- ANDO, S. and IBA, H. (2003) Artificial Immune System for Classification of Gene Expression Data. In: E. Cantú-Paz et al., eds., GECCO 2003, LNCS 2724, Springer, 1926-937.
- BANDYOPADHYAY, S., PAL, S.K. and ARUNA, B. (2004) Multiobjective GAs, quantitative indices, and pattern classification. IEEE Trans. SMC B, 34, 2088-2099.
- BERETA, M. and BURCZYŃSKI, T. (2006) Immune K-means: A novel immune algorithm for data clustering and multiple-class discrimination. In: Evolutionary Computation and Global Optimization 2006. Prace Naukowe, Elektronika. Warsaw Univ. of Technology Publishing House Warszawa, 49-60.
- BERETA, M. and BURCZYŃSKI, T. (2007) Comparing binary and real-valued coding in hybrid immune algorithm for feature selection and classification of ECG signals. Eng. Appl. Artif. Intell. 20 (5), 571-585.
- BISHOP, C.M. (1995) Neural Networks for Pattern Recognition. Oxford University Press.
- BREIMAN, L. (1996) Bagging predictors. Mach. Learn. 24 (2), 123-140.
- BREIMAN, L. (2001) Random Forests. Mach. Learn. 45 (1), 5-32.
- BREIMAN, L., FRIEDMAN, J.H., OLSHEN, R.A. and STONE, C.J. (1984) Classification and Regression Trees. Statistics/Probability Series, Wadsworth Publishing Company, Belmont, California, U.S.A.
- CARTER, J.H. (2000) The Immune System as a Model for Pattern Recognition and Classification. Journal of the American Medical Informatics Assocation 7 (1), 28-41.
- DASGUPTA, D. (1998) Artificial Immune Systems and Their Applications. Springer-Verlag New York, Inc., Secaucus, NJ, USA.
- GABRYS, B. and RUTA, D. (2006) Genetic algorithms in classifier fusion. Applied Soft Computing 6, 337-347.
- JING, X. and ZHANG, D. (2003) Face recognition based on linear classifiers combination. Neurocomputing 50, 485-488.
- KIM, Y.S., STREET, W.N. and MENCZER, F. (2006) Optimal ensemble construction via meta-evolutionary ensembles. Expert Systems with Applications 30, 705-714.
- KIM, Y.W. and OH, I.-S. (2008) Classifier ensemble selection using hybrid genetic algorithms. Pattern Recogn. Lett. 29 (6), 796-802, doi:http://dx.doi.org/10.1016/j.patrec.2007.12.013.
- KRETOWSKA, M. (2008) Ensemble of Dipolar Neural Networks in Application to Survival Data. In: ICAISC `08: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing. Springer-Verlag, Berlin, Heidelberg, 78-88.
- PAL, S.K., BANDYOPADHYAY, S. and MURTHY, C.A. (1998) Genetic algorithms for generation of class boundaries. IEEE Trans. SMC B 28, 816-828.
- RUTA, D. and GABRYS, B. (2005) Classifier selection for majority voting. In-formation Fusion 6, 63-81.
- SCHAPIRE, R. (2001) The boosting approach to machine learning: An overview. URL http://citeseer.ist.psu.edu/schapire02boosting.html.
- VAPNIK, V.N. (1995) The Nature of Statistical Learning Theory, Springer-Verlag New York, Inc., New York, NY, USA.
- WANG, X. and WANG, H. (2006) Classification by evolutionary ensembles, Pattern Recogn., 39(4), 595-607, doi:http://dx.doi.org/10.1016/j.patcog.2005.09.016.
- WATKINS, A. (2005) Exploiting Immunological Metaphors in the Development of Serial, Parallel, and Distributed Learning Algorithms. Ph.D. thesis University of Kent.
- WATKINS, A.. TIMMIS, J. and BOGGESS, L. (2004) Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Machine Learning Algorithm. Genetic Programming and Evolvable Machines 5 (3), 291-317, URL citeseer.ist.psu.edu/watkins03artificial.html.
- ZHANG, Y. and BHATTACHARYYA, S. (2004) Genetic programming in classifying large-scale data: an ensemble method. Inf. Sci. 163 (1-3), 85-101, doi:http://dx.doi.org/10.1016/j.ins.2003.03.028.
Typ dokumentu
Bibliografia
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