Abstract
In this paper, we propose a new approach of classification based on the artificial immune Dendritic Cell Algorithm (DCA). Many researches have demonstrated the promising DCA classification results in many real world applications. Despite of that, it was shown that the DCA has a main limitation while performing its classification task. To classify a new data item, the expert knowledge is required to calculate a set of signal values. Indeed, to achieve this, the expert has to provide some specific formula capable of generating these values. Yet, the expert mandatory presence has received criticism from researchers. Therefore, in order to overcome this restriction, we have proposed a new version of the DCA combined with the K-Nearest Neighbors (KNN). KNN is used to provide a new way to calculate the signal values independently from the expert knowledge. Experimental results demonstrate the significant performance of our proposed solution in terms of classification accuracy, in comparison to several state-of-the-art classifiers, while avoiding the mandatory presence of the expert.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
Greensmith, J., Aickelin, U.: Dendritic cells for syn scan detection. In: GECCO, pp. 49–56 (2007)
Chelly, Z., Elouedi, Z.: Hybridization schemes of the fuzzy dendritic cell immune binary classifier based on different fuzzy clustering techniques. In: New Generation Compution, vol. 33(1), pp. 1–31. Ohmsha, Chiyoda-ku (2015)
Ghosh, A.: On optimum choice of k in nearest neighbor classification. Comput. Stat. Data Anal. 50(11), 3113–3123 (2006)
Chelly, Z., Elouedi, Z.: Supporting fuzzy-rough sets in the dendritic cell algorithm data pre-processing phase. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part II. LNCS, vol. 8227, pp. 164–171. Springer, Heidelberg (2013)
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)
Asuncion, A., Newman, D.J.: UCI machine learning repository, (2007). http://mlearn.ics.uci.edu/mlrepository.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ali, K.B., Chelly, Z., Elouedi, Z. (2015). A New Version of the Dendritic Cell Immune Algorithm Based on the K-Nearest Neighbors. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_76
Download citation
DOI: https://doi.org/10.1007/978-3-319-26532-2_76
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26531-5
Online ISBN: 978-3-319-26532-2
eBook Packages: Computer ScienceComputer Science (R0)