Abstract
We propose a hybrid hierarchical classifier that solves multi-class problems in high dimensional space using a set of binary classifiers arranged as a tree in the space of classes. It incorporates good aspects of both the binary hierarchical classifier (BHC) and the margin tree algorithm, and is effective over a large range of (sample size, input dimensionality) values. Two aspects of the proposed classifier are empirically evaluated on two hyperspectral datasets: the structure of the class hierarchy and the classification accuracies. The proposed hybrid algorithm is shown to be superior on both aspects when compared to other binary classification trees, including both the BHC and the margin tree algorithm.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artifical Intelligence Research 2, 263 (1995)
Kumar, S., Ghosh, J., Crawford, M.M.: Hierarchical fusion of multiple classifiers for hyperspectral data analysis. Pattern Analysis & Applications 5(2), 210–220 (2002)
Tibshirani, R., Hastie, T.: Margin trees for high-dimensional classification. J. Mach. Learn. Res. 8, 637–652 (2007)
Rajan, S., Ghosh, J.: An empirical comparison of hierarchical vs. two-level approaches to multiclass problems. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 283–292. Springer, Heidelberg (2004)
Kumar, S., Ghosh, J., Crawford, M.M.: Best-bases feature extraction algorithms for classification of hyperspectral data. IEEE Trans. on Geosci. and Remote Sens. 39(7), 1368–1379 (2001)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2001)
Cover, T.M.: Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers EC-14(3), 326–334 (1965)
Landgrebe, D.: Hyperspectral image data analysis. IEEE Signal Processing Magazine 19, 17–28 (2002)
Morgan, J.T.: Adaptive hierarchical classifier with limited training data. Ph.D thesis, Univ. of Texas at Austin (2002)
Ham, J., Chen, Y., Crawford, M.M., Ghosh, J.: Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans. Geosci. and Remote Sens. 43(3), 492–501 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jun, G., Ghosh, J. (2009). Hybrid Hierarchical Classifiers for Hyperspectral Data Analysis. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-02326-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02325-5
Online ISBN: 978-3-642-02326-2
eBook Packages: Computer ScienceComputer Science (R0)