Abstract
In this paper, we propose a new approach of data pre- processing based on rough set theory for the Dendritic Cell Algorithm (DCA). Our hybrid immune inspired model, denoted QR-DCA, is based on the functioning of dendritic cells within the framework of rough set theory and more precisely, on the QuickReduct algorithm. As the DCA data pre-processing phase is divided into two sub-steps, feature selection and signal categorization, our QR-DCA model selects the right features for the DCA classification task and categorizes each one of them to its specific signal category. This is achieved while preserving the same DCA main characteristic which is its lightweight in terms of running time. Results show that our new approach generates good classification results. We will also compare our QR-DCA to other rough DCA models to show that our new approach outperforms them in terms of classification accuracy while keeping the worthy characteristics expressed by the DCA.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Greensmith, J., Aickelin, U., Cayzer, S.: Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 153–167. Springer, Heidelberg (2005)
Gu, F., Greensmith, J., Oates, R., Aickelin, U.: Pca 4 dca: The application of principal component analysis to the dendritic cell algorithm. CoRR (2010)
Cantú-Paz, E.: Feature Subset Selection, Class Separability, and Genetic Algorithms. In: Deb, K., Tari, Z. (eds.) GECCO 2004, Part I. LNCS, vol. 3102, pp. 959–970. Springer, Heidelberg (2004)
Chelly, Z., Elouedi, Z.: RC-DCA: A New Feature Selection and Signal Categorization Technique for the Dendritic Cell Algorithm Based on Rough Set Theory. In: Coello Coello, C.A., Greensmith, J., Krasnogor, N., Liò, P., Nicosia, G., Pavone, M. (eds.) ICARIS 2012. LNCS, vol. 7597, pp. 152–165. Springer, Heidelberg (2012)
Greensmith, J., Aickelin, U.: The Deterministic Dendritic Cell Algorithm. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 291–302. Springer, Heidelberg (2008)
Lotze, M.T., Thomson, A.W.: Dendritic Cells: Biology and Clinical Applications, 2nd edn., vol. 794 (2001)
Jolliffe, I.T.: Principal component analysis. Springer, New York (2002)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Science 11, 341–356 (1982)
Jensen, R., Shen, Q.: A rough set-aided system for sorting www bookmarks. In: Zhong, N., et al. (eds.) Web Intelligence: Research and Development, pp. 95–105 (2001)
Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)
Chelly, Z., Elouedi, Z.: RST-DCA: A Dendritic Cell Algorithm Based on Rough Set Theory. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part III. LNCS, vol. 7665, pp. 480–487. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chelly, Z., Elouedi, Z. (2013). QR-DCA: A New Rough Data Pre-processing Approach for the Dendritic Cell Algorithm. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-37213-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37212-4
Online ISBN: 978-3-642-37213-1
eBook Packages: Computer ScienceComputer Science (R0)