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PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm

Gu, Feng; Greensmith, Julie; Oates, Robert; Aickelin, Uwe

Authors

Feng Gu fxg@cs.nott.ac.uk

Julie Greensmith jqg@cs.nott.ac.uk

Robert Oates rxo@cs.nott.ac.uk

Uwe Aickelin uwe.aickelin@nottingham.ac.uk



Abstract

As one of the newest members in the field of articial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is
based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by
the DCA. This data preprocessing phase has received the criticism of having manually over-fitted the data to the algorithm, which is undesirable. Therefore, in this
paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classication results. The integrated system is tested with a biometrics dataset for the stress
recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.

Peer Reviewed Peer Reviewed
APA6 Citation Gu, F., Greensmith, J., Oates, R., & Aickelin, U. PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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