Abstract
Training and application of prototype based learning approaches such as Learning Vector Quantization, Radial Basis Function networks, and Supervised Neural Gas require the use of distance metrics to measure the similarities between feature vectors as well as class prototypes. While the Euclidean distance is used in many cases, the highly correlated features within the hyperspectral representation and the high dimensionality itself favor the use of more sophisticated distance metrics. In this paper we first investigate the role of different metrics for successful classification of hyperspectral data sets from real-world classification tasks. Second, it is shown that considerable performance gains can be achieved by a classification system that combines a number of prototype based models trained on differently parametrized divergence measures. Data sets are tested using a number of different combination strategies.
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References
Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420–434. Springer, Heidelberg (2000)
Al-Ani, A., Deriche, M.: A new technique for combining multiple classifiers using the dempster-shafer theory of evidence. Journal of Artificial Intelligence Research 17, 333–361 (2002)
Backhaus, A., Bollenbeck, F., Seiffert, U.: Robust classification of the nutrition state in crop plants by hyperspectral imaging and artificial neural networks. In: Proc. 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Lisboa, Portugal (2011)
Bishop, C.M., Svensén, M.: Hierarchical Mixtures of Experts. In: 19th Conference on Uncertainty in Artificial Intelligence (2003)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Chen, X., Li, Y., Harrison, R., Zhang, Y.-Q.: Type-2 fuzzy logic-based classifier fusion for support vector machines. Applied Soft Computing 8(3), 1222–1231 (2008)
Didaci, L., Fumera, G., Roli, F.: Diversity in Classifier Ensembles: Fertile Concept or Dead End? In: Zhou, Z.-H., Roli, F., Kittler, J. (eds.) MCS 2013. LNCS, vol. 7872, pp. 37–48. Springer, Heidelberg (2013)
Duin, R.P.W.: The combining classifier: to train or not to train. In: ICPR (2002)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: European Conference on Computational Learning Theory, pp. 23–37 (1995)
Geweniger, T., Kästner, M., Villmann, T.: Optimization of parametrized divergences in fuzzy c-means. In: ESANN (2011)
Hammer, B., Strickert, M., Villmann, T.: Supervised Neural Gas with general similarity measure. Neural Processing Letters 21, 21–44 (2005)
Hammer, B., Villmann, T.: Generalized relevance learning vector quantization. Neural Networks 15, 1059–1068 (2002)
Jordan, M.I., Jacobs, R.A.: Hierarchical Mixtures of experts and the EM-algorithm. Neural Computation 6(2), 181–214 (1994)
Kang, S., Park, S.: A fusion neural network classifier for image classification. Pattern Recogn. Lett. 30(9), 789–793 (2009)
Kästner, M., Backhaus, A., Geweniger, T., Haase, S., Seiffert, U., Villmann, T.: Relevance learning in unsupervised vector quantization based on divergences. In: Laaksonen, J., Honkela, T. (eds.) WSOM 2011. LNCS, vol. 6731, pp. 90–100. Springer, Heidelberg (2011)
Khreich, W., Granger, E., Miri, A., Sabourin, R.: Iterative Boolean combination of classifiers in the ROC space: An application to anomaly detection with HMMs. Pattern Recognition 43(8), 2732–2752 (2010)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Knauer, U., Seiffert, U.: A Comparison of Late Fusion Methods for Object Detection. In: IEEE International Conference on Image Processing, pp. 1–8 (2013)
Knauer, U., Seiffert, U.: Cascaded Reduction and Growing of Result Sets for Combining Object Detectors. In: Zhou, Z.-H., Roli, F., Kittler, J. (eds.) MCS 2013. LNCS, vol. 7872, pp. 121–133. Springer, Heidelberg (2013)
Mendenhall, M.J., Merényi, E.: Relevance-based feature extraction for hyperspectral images. IEEE Transactions on Neural Networks 19(4), 658–672 (2008)
Moody, J., Darken, C.J.: Fast learning in networks of locally tuned processing units. Neural Computation 1, 281–294 (1989)
Mwebaze, E., Schneider, P., Schleif, F.-M., Haase, S., Villmann, T., Biehl, M.: Divergence based Learning Vector Quantization. In: Verleysen, M. (ed.) 18th European Symposium on Artificial Neural Networks (ESANN 2010), pp. 247–252. d-side publishing (2010)
Peltonen, J., Klami, A., Kaski, S.: Learning more accurate metrics for self-organizing maps. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 999–1004. Springer, Heidelberg (2002)
Płoński, P., Zaremba, K.: Improving performance of self-organising maps with distance metric learning method. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 169–177. Springer, Heidelberg (2012)
Schneider, P., Schleif, F.-M., Villmann, T., Biehl, M.: Generalized matrix learning vector quantizer for the analysis of spectral data. In: ESANN, pp. 451–456 (2008)
Villmann, T., Haase, S.: Divergence based vector quantization of spectral data. In: 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–4 (2010)
Villmann, T., Haase, S.: Divergence-based vector quantization. Neural Comput. 23(5), 1343–1392 (2011)
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Knauer, U., Backhaus, A., Seiffert, U. (2014). Beyond Standard Metrics – On the Selection and Combination of Distance Metrics for an Improved Classification of Hyperspectral Data. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_16
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DOI: https://doi.org/10.1007/978-3-319-07695-9_16
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