Immune K-Means : a novel immune algorithm for data clustering and multiple-class discrimination

• Autorzy: Bereta M. , Burczyński T.
• Konferencja: Evolutionary Computation and Global Optimization 2006 / National Conference (9 ; 31.05-2.06.2006 ; Murzasichle, Poland)
• Języki publikacji: EN

Abstrakty

EN

This paper presents a novel approach to data clustering and multiple-class classification problems. The proposed method is based on a metaphor derived from immune systems, the clonal selection paradigm. A novel clonal selection algorithm - Immune K-Means, is proposed. The proposed system is able to cluster real valued data efficiently and correctly, dynamically estimating the number of clusters. In classification problems discrimination among classes is based on the k-nearest neighbor method. Two different types of suppression are proposed. They enable the evolution of different populations of lymphocytes well suited to a given problem : clustering or classification. The first type of suppression enables the lymphocytes to discover the data distribution while the second type of suppression focuses the lymphocytes on the classes' boundaries. Primary results on artificial data and a real-world benchmark dataset (Fisher's Iris Database) as well as a discussion of the parameters of the algorithm are given.

Słowa kluczowe

EN

data clustering; multiple-class discrimination; artificial Immune Systems; AIS; clonal selection algorithm; Immune K-Means

PL

grupowanie; analiza skupień; rozróżnianie wieloklasowe; sztuczny system immunologiczny; algorytm selekcji klonalnej; Immune K-Means

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2006
• Tom: z. 156
• Strony: 49--60
• Opis fizyczny: Bibliogr. 8 poz., tab., rys., wykr.

Twórcy

autor

Bereta M.

autor

Burczyński T.

• Cracow University of Technology, Institute of Computer Modeling, Artificial Intelligence Department, Cracow, Poland,

Bibliografia

• [1] M. Bereta and T. Burczyński. Hybrid immune algorithm for feature selection and classification of ECG signals. In T. Burczyński, W. Cholewa and W. Moczulski, editors, Recent Developments in Artificial Intelligence Methods, AI-METH Series, pages 25-28, Gliwice, 2005.
• [2] D. Dasgupta and S. Forrest. Novelty detection in time series data using ideas from immunology. In ISCA 5th International Conference on Intelligent Systems, pages 19-21, Reno, Nevada, June 1996.
• [3] D. Dasgupta and S. Forrest. An Anomaly Detection Algorithm Inspired by the Immune System, chapter 14 in the book entitled Artificial Immune Systems and Their Applications, pages 262-277. Springer-Verlag, Inc., January 1999.
• [4] L.N. de Castro and J. Timmis. Artificial Immune Systems: A New Computational Approach. Springer-Verlag, London, UK., September 2002.
• [5] D. Goodman, L. Boggess and A. Watkins. Artificial immune system classification of multiple-class problems. In Artificial Neural Networks in Engineering (ANNIE-2002), 2002.
• [6] P. D'haeseleer, S. Forrest and P. Helman. An immunological approach to change detection: algorithms, analysis and implications. In Proceedings of the 1996 IEEE Symposium on Computer Security and Privacy (1996).
• [7] J. Timmis and M.J. Neal. A Resource Limited Artificial Immune System for Data Analysis. Research and Development in Intelligent Systems XVII, pages 19-32, December 2000. Proceedings of ES2000, Cambridge, UK.
• [8] Andrew Watkins, Jon Timmis and Lois Boggess. Artificial immune recognition system (AIRS): An immune-inspired supervised machine learning algorithm. Genetic Programming and Evolvable Machines, 5(3):291-317, September 2004.