Non-Euclidean distance measures in AIRS, an artificial immune classification system

The AIRS classifier, based on principles derived from resource limited artificial immune systems, performs consistently well over a broad range of classification problems. This paper explores the effects of adding nonEuclidean distance measures to the basic AIRS algorithm using four well-known publicly available classification problems having various proportions of real, discrete, and nominal features.

[1]  Thomas Richardson,et al.  Interpretable Boosted Naïve Bayes Classification , 1998, KDD.

[2]  Jon Timmis,et al.  Artificial Immune Recognition System (AIRS): Revisions and Refinements , 2002 .

[3]  Geoffrey I. Webb.  Further Experimental Evidence against the Utility of Occam's Razor , 1996, J. Artif. Intell. Res..

[4]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[5]  Jonathan Timmis,et al.  A resource limited artificial immune system for data analysis , 2001, Knowl. Based Syst..

[6]  Lois Boggess,et al.  An investigation into the source of power for AIRS, an artificial immune classification system , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[7]  A. B. Watkins,et al.  A resource limited artificial immune classifier , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[8]  Jonathan Timmis,et al.  Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm , 2004, Genetic Programming and Evolvable Machines.

[9]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[10]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[11]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[12]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .