Abstract
The establishment of the learning data base is a long and tedious task that must be carried out before starting the classification process. An Evidential KNN (EvKNN) has been developed in order to help the user, which proposes the “best” samples to label according to a strategy. However, at the beginning of this task, the classes are not clearly defined and are represented by a number of labeled samples smaller than the k required samples for EvKNN. In this paper, we propose to take into account the available information on the classes using an adapted evidential model. The algorithm presented in this paper has been tested on the classification of an image collection.
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
Goëau, H., Rombaut, M., Pellerin, D., Buisson, O.: Multi-labeled image classification by TBM active learning. In: Workshop on the Theory of Belief Function, April 1-2, Brest (2010)
Denœux, T.: A k-nearest neighbor classification rule based on dempster-shafer theory. IEEE Trans. on SMC, 804–813 (1995)
Qi, G.-J., Hua, X.-S., Rui, Y., Tang, J., Zhang, H.-J.: Two-dimensional multi-label active learning with an efficient online adaptation model for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 99(1) (2008)
Goëau, H.: Structuration de collections d’images par apprentissage actif crédibiliste, PhD, Université Joseph Fourier, Grenoble, France (May 2009)
Crucianu, M., Ferecatu, M., Boujemaa, N.: Relevance feedback for image retrieval: a short survey. Report of the DELOS2 European Network of Excellence (FP6) (2004), citeseer.ist.psu.edu/crucianu04relevance.html
Smets, P.: Decision making in tbm: the necessity of the pignistic transformation. Journal of Approximate Reasonning 38(2), 133–147 (2005)
Brinker, K.: Incorporating diversity in active learning with support vector machines. In: Proceedings of the 20th International Conference on Machine Learning, pp. 59–66. AAAI Press (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tim, S.C.W., Rombaut, M., Pellerin, D. (2012). Adaptive Initialization of a EvKNN Classification Algorithm. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-29461-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29460-0
Online ISBN: 978-3-642-29461-7
eBook Packages: EngineeringEngineering (R0)