Abstract
This paper investigates automated benthic macroinvertebrate identification and classification with multi-class support vector machines. Moreover, we examine, how the feature selection effects results, when one-vs-one and one-vs-all methods are used. Lastly, we explore what happens for the number of tie situations with different kernel function selections. Our wide experimental tests with three feature sets and seven kernel functions indicated that one-vs-one method suits best for the automated benthic macroinvertebrate identification. In addition, we obtained clear differences to the number of tie situations with different kernel funtions. Furthermore, the feature selection had a clear influence on the results.
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
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Chauduri, A., De, K., Chatterjee, D.: A Comparative Study of Kernels for the Multi-class Support Vector Machine. In: Guo, M., Zhao, L., Wang, L. (eds.) Fourth International Conference of Natural Computation (ICNC 2008), vol. 2, pp. 3–7 (2008)
Christiani, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)
Debnath, R., Takahide, N., Takahashi, H.: A decision based one-against-one method for multi-class support vector machine. Pattern Analysis Applications 7, 164–175 (2004)
ImageJ: public domain Java-based image processing program, http://rsbweb.nih.gov/ij/docs/index.html
Joutsijoki, H., Juhola, M.: Kernel selection and its consequense to the number of ties in majority voting method (2011) (submitted)
Kahsay, L., Schwenker, F., Palm, G.: Comparison of multiclass svm decomposition schemes for visual object recognition. In: Kropatsch, W., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 334–341. Springer, Heidelberg (2005)
Kiranyaz, S., Gabbouj, M., Pulkkinen, J., Ince, T., Meissner, K.: Classification and Retrieval on Macroinvertebrate Image Databases using Evolutionary RBF Neural Networks. In: Proceedings of the International Workshop on Advanced Image Technology, Kuala Lumpur, Malaysia (2010)
Kiranyaz, S., Ince, T., Pulkkinen, J., Gabbouj, M., Ärje, J., Kärkkäinen, S., Tirronen, V., Juhola, M., Turpeinen, T., Meissner, K.: Classification and retrieval on macroinvertabrate image databases. Computers in Biology and Medicine 41, 463–472 (2011)
Kiranyaz, S., Gabbouj, M., Pulkkinen, J., Ince, T., Meissner, K.: Network of evolutionary binary classifiers for classification and retrieval in macroinvertebrate databases. In: Proceedings of 2010 IEEE 17th International Conference on Image Processing (ICIP), pp. 2257–2260 (2010)
Li, Y., Dorai, C.: Instructional Video Content Analysis Using Audio Information. IEEE Transactions on Audio, Speech, and Language Processing 14(6), 2264–2274 (2006)
Liu, J., Xie, L.: SVM-Based Automatic Classification of Musical Instruments. In: International Conference on Intelligent Computation Technology and Automation (ICICTA 2010), vol. 3, pp. 669–673 (2010)
Lytle, D.A., Martinez-Muñoz, G., Zhang, W., Larios, N., Shapiro, L., Paasch, R., Moldenke, A., Mortensen, E.N., Todorovic, S., Dietterich, T.G.: Automated processing and identification of benthic invertebrate samples. Journal of North American Benthological Society 29(3), 867–874 (2010)
Rifkin, R., Klautau, A.: In defense of one-vs-all classification. Journal of Machine Learning 5, 101–141 (2004)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Processing Letters 9, 293–300 (1999)
Thorsten, J.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Tirronen, V., Caponio, A., Haanpää, T., Meissner, K.: Multiple order gradient feature for macroinvertebrate identification using support vector machines. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) ICANNGA 2009. LNCS, vol. 5495, pp. 489–497. Springer, Heidelberg (2009)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (2000)
Ärje, J., Kärkkäinen, S., Meissner, K., Turpeinen, T.: Statistical classification and proportion estimation - an application to macroinvertebrate image database. In: IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), Kittilä, Finland, pp. 373–378 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Joutsijoki, H., Juhola, M. (2011). Comparing the One-vs-One and One-vs-All Methods in Benthic Macroinvertebrate Image Classification. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-23199-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23198-8
Online ISBN: 978-3-642-23199-5
eBook Packages: Computer ScienceComputer Science (R0)