Evolving ensembles of linear classifiers by means of clonal selection algorithm

• Autorzy: Bereta M. , Burczyński T.
• Języki publikacji: EN

Abstrakty

EN

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

EN

artificial immune systems; clonal selection; linear classifiers; bagging; boosting

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2010
• Tom: Vol. 39, no 2
• Strony: 325--342
• Opis fizyczny: Bibliogr. 23 poz., wykr.

Twórcy

autor

Bereta M.

autor

Burczyński T.

• 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.