PCA 4 DCA: The Application Of Principal Component Analysis To The Dendritic Cell Algorithm

TL;DR

This paper explores applying Principal Component Analysis to automate data preprocessing in the Dendritic Cell Algorithm, aiming to improve classification accuracy without manual data tuning, tested on a biometrics stress recognition dataset.

Contribution

It introduces PCA integration into DCA to automate input categorization, reducing manual data preprocessing and potential overfitting in artificial immune system applications.

Findings

PCA successfully automates data categorization in DCA.

The integrated system improves classification accuracy.

Experimental results confirm the effectiveness of PCA in DCA.

Abstract

As one of the newest members in the field of artificial 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-?tted 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.