Abstract
In this paper we examined the suitability of the Directed Acyclic Graph Support Vector Machine (DAGSVM) and Directed Acyclic Graph k-Nearest Neighbour (DAGKNN) method in classification of the benthic macroinvertebrate samples. We divided our 50 species dataset into five ten species groups according to their group sizes. We performed extensive experimental tests with every group, where DAGSVM was tested with seven kernel functions and DAGKNN with four measures. Feature selection was made by the scatter method [8]. Results showed that the quadratic and RBF kernel functions were the best ones and in the case of DAGKNN all measures produced quite similar results. Generally, the DAGSVM gained higher accuracies than DAGKNN, but still DAGKNN is a respectable option in benthic macroinvertebrate classification.
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References
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)
Cover, T.M.: Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers EC-14 (1965)
ImageJ: public domain Java-based image processing program, http://rsbweb.nih.gov/ij/docs/index.html
Joutsijoki, H., Juhola, M.: Automated benthic macroinvertebrate identification with decision acyclic graph support vector machines. In: Proceedings of the 2nd IASTED International Conference Computational Bioscience (CompBio 2011), Cambridge, United Kingdom, pp. 323–328 (2011)
Joutsijoki, H., Juhola, M.: Comparing the one-vs-one and one-vs-all methods in benthic macroinvertebrate image classification. In: Perner, P. (ed.) MLDM 2011. LNCS (LNAI), vol. 6871, pp. 399–413. Springer, Heidelberg (2011)
Joutsijoki, H., Juhola, M.: Kernel selection and its consequence to the number of ties in majority voting method. Artificial Intelligence Review (in press), doi:10.1007/s10462-011-9281-3
Juhola, M., Siermala, M.: A scatter method for data and variable importance evaluation. Integrated Computer-Aided Engineering 19(2), 137–149 (2012)
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 macroinvertebrate image databases. Computers in Biology and Medicine 41(7), 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)
Larios, N., Deng, H., Zhang, W., Sarpola, M., Yuen, J., Paasch, R., Moldenke, A., Lytle, D.A., Correa, S.R., Mortensen, E.N., Shapiro, L.G., Dietterich, T.G.: Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects. Machine Vision and Applications 19(2), 105–123 (2008)
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)
Platt, J.C., Christiani, N., Shawe-Taylor, J.: Large margin dags for multiclass classification. In: Advances in Neural Information Processing Systems (NIPS 1999) 12, Denver, USA, pp. 547–553 (2000)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Processing Letters 9, 293–300 (1999)
Tirronen, V., Caponio, A., Haanpää, T., Meissner, K.: Multiple Order Gradient Feature for Macro-Invertebrate Identification Using Support Vector Machines. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) ICANNGA 2009. LNCS, vol. 5495, pp. 489–497. Springer, Heidelberg (2009)
Ärje, J., Kärkkäinen, S., Meissner, K., Turpeinen, T.: Statistical classification and proportion estimation - an application to macroinvertebrate image database. In: 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), Kittilä, Finland, pp. 373–378 (2010)
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Joutsijoki, H., Juhola, M. (2012). DAGSVM vs. DAGKNN: An Experimental Case Study with Benthic Macroinvertebrate Dataset. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_35
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DOI: https://doi.org/10.1007/978-3-642-31537-4_35
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