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Evolutionary Intelligence

, Volume 1, Issue 2, pp 159–169 | Cite as

Exploratory data analysis with artificial immune systems

  • Ying Wu
  • Colin Fyfe
Research Paper

Abstract

We use a modified version of the CLONALG algorithm to perform exploratory data analysis. Since we wish to compare results from a number of methods, we only report on linear projections which have unique solutions. We incorporate a type of Gram Schmidt orthogonalisation [15] into the affinity maturation process to capture multiple components. We combine the new algorithm with reinforcement learning [17, 20] and with cross entropy maximization [13, 19]. Finally we combine several different non-standard adaptation methods using bagging and show that we get reliable convergence to accurate filters.

Keywords

Independent Component Analysis Canonical Correlation Analysis Independent Component Analysis Artificial Immune System Linear Projection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.School of ComputingThe University of the West of ScotlandPaisleyScotland

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