PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm
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 th...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2009
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| Online Access: | https://eprints.nottingham.ac.uk/1283/ |
| _version_ | 1848790576586031104 |
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| author | Gu, Feng Greensmith, Julie Oates, Robert Aickelin, Uwe |
| author_facet | Gu, Feng Greensmith, Julie Oates, Robert Aickelin, Uwe |
| author_sort | Gu, Feng |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:14:49Z |
| format | Conference or Workshop Item |
| id | nottingham-1283 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:14:49Z |
| publishDate | 2009 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-12832020-05-04T20:26:50Z https://eprints.nottingham.ac.uk/1283/ PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm Gu, Feng Greensmith, Julie Oates, Robert Aickelin, Uwe 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. 2009 Conference or Workshop Item PeerReviewed Gu, Feng, Greensmith, Julie, Oates, Robert and Aickelin, Uwe (2009) PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm. In: 9th Annual Workshop on Computational Intelligence (UKCI 2009), 7-9 Sept. 2009, Nottingham, UK. |
| spellingShingle | Gu, Feng Greensmith, Julie Oates, Robert Aickelin, Uwe PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm |
| title | PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm |
| title_full | PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm |
| title_fullStr | PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm |
| title_full_unstemmed | PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm |
| title_short | PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm |
| title_sort | pca 4 dca: the application of principal component analysis to the dendritic cell algorithm |
| url | https://eprints.nottingham.ac.uk/1283/ |