Abstract
Despite the good results provided by Dynamic Classifier Selection (DCS) mechanisms based on local accuracy in a large number of applications, the performances are still capable of improvement. As the selection is performed by computing the accuracy of each classifier in a neighbourhood of the test pattern, performances depend on the shape and size of such a neighbourhood, as well as the local density of the patterns. In this paper, we investigated the use of neighbourhoods of adaptive shape and size to better cope with the difficulties of a reliable estimation of local accuracies. Reported results show that performance improvements can be achieved by suitably tuning some additional parameters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Xu, L., Krzyzak, A., Suen, C.Y.: Methods for combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. on Systems, Man, and Cybernetics 22, 418–435 (1992)
Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifiers systems. IEEE Trans. on Pattern Analysis and Machine Intelligence 16(1), 66–75 (1994)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)
Windeatt, T., Roli, F. (eds.): MCS 2003. LNCS, vol. 2709. Springer, Heidelberg (2003)
Kuncheva, L.I.: Switching between selection and fusion in combining classifiers: An experiment. IEEE Transactions on SMC, Part B 32(2), 146–156 (2002)
Giacinto, G., Roli, F.: Dynamic Classifier Selection. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 177–189. Springer, Heidelberg (2000)
Woods, K., Kegelmeyer, W.P., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(4), 405–410 (1997)
Giacinto, G., Roli, F.: Adaptive Selection Of Image Classifiers. In: Del Bimbo, A. (ed.) ICIAP 1997. LNCS, vol. 1311, pp. 38–45. Springer, Heidelberg (1997)
Smits, P.C.: Multiple Classifier Systems for Supervised Remote Sensing Image Classification Based on Dynamic Classifier Selection. IEEE Transactions on Geoscience and Remote Sensing 40(4), 801–813 (2002)
Dasarathy, B.V. (ed.): Nearest neighbor(nn) norms: NN pattern classification techniques. IEEE Computer Society Press, Los Alamitos (1991)
Hastie, T., Tibshirani, R.: Discriminant Adaptive Nearest Neighbor Classification. IEEE Trans. on Pattern Analysis and Machine Intelligence 18(6), 607–615 (1996)
Hsieh, P.F., Landgrebe, D.: Classification of high dimensional data, Ph.D., School Elect. Comput. Eng., Purdue Univ., West Lafayette, IN (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Didaci, L., Giacinto, G. (2004). Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-25966-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22144-9
Online ISBN: 978-3-540-25966-4
eBook Packages: Springer Book Archive