Abstract
Support vector machines are a relatively new classification method which has nowadays established a firm foothold in the area of machine learning. It has been applied to numerous targets of applications. Automated taxa identification of benthic macroinvertebrates has got generally very little attention and especially using a support vector machine in it. In this paper we investigate how the changing of a kernel function in an SVM classifier effects classification results. A novel question is how the changing of a kernel function effects the number of ties in a majority voting method when we are dealing with a multi-class case. We repeated the classification tests with two different feature sets. Using SVM, we present accurate classification results proposing that SVM suits well to the automated taxa identification of benthic macroinvertebrates. We also present that the selection of a kernel has a great effect on the number of ties.
Similar content being viewed by others
References
Ärje J, Kärkkäinen S, Meissner K, Turpeinen T (2010) Statistical classification and proportion estimation—an application to a macroinvertebrate image database. In: Proceedings 20th international IEEE workshop on machine learning for signal processing (MLSP 2010), Kittilä, Finland, pp 373–378
Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2: 121–167
Christiani N, Shawe-Taylor J (2003) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20: 273–297
Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans on Electr Comp EC-14
Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9: 1871–1874
Fletcher T (2009) Support vector machines explained. Available: http://www.cs.ucl.ac.uk/staff/T.Fletcher/. Accessed 17 June 2011
Hong JH, Cho SB (2008) A probabilistic multi-class strategy of one-vs.-rest support vector machines for cancer classification. Neurocomputing 71: 3275–3281
Hong JH, Min JK, Cho UK, Cho SB (2008) Fingerprint classification using one-vs-all support vector machines dynamically ordered with naïve bayes classifiers. Pattern Recognit 41: 662–671
Howley T, Madden MG (2004) The genetic evolution of kernels for support vector machine classifiers. In: Proceedings of AICS-2004 15th Irish Conference on Artificial Intelligence & Cognitive Science
ImageJ: public domain Java-based image processing program. Available: http://rsbweb.nih.gov/ij/docs/index.html
Kiranyaz S, Gabbouj M, Pulkkinen J, Ince T, Meissner K (2010) Classification and retrieval on macroinvertebrate image databases using evolutionary RBF neural networks. In: Proceedings of the International Workshop on Advanced Image Technology (IWAIT), 11–12 January 2010 Kuala Lumpur, Malaysia
Kiranyaz S, Ince T, Pulkkinen J, Gabbouj M, Ärje J, Kärkkäinen S, Tirronen V, Juhola M, Turpeinen T, Meissner K (2011) Classification and retrieval on macroinvertebrate image databases. J Comput Biol Med 41(7): 463–472
Larios N, Deng H, Zhang W, Sarpola M, Yuen J, Paasch R, Moldenke A, Lytle DA, Correa SR, Mortensen EN, Shapiro LG, Diettrich TG (2008) Automated insect identification through concatenated histograms of local appearance features: features vector generation and region detection for deformable objects. Mach Vision Appl 19(2): 105–123
Lytle DA, Martínez-Muñoz G, Zhang W, Larios N, Shapiro L, Paasch R, Moldenke A, Mortensen EN, Todorovic S, Diettrich TG (2010) Automated processing and identification of benthic invertebrate samples. J N Am Benthol Soc 29(3): 867–874
Moguerza JM, Muñoz A (2006) Support vector machines with applications. Stat Sci 21(3): 332–336
Platt JC (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. In: Technical Report MSR-TR-98-14 Microsoft Research
Riverlife project. Available in Finnish: http://www.ymparisto.fi/riverlife, Accessed 20 June 2011
Sarpola MJ, Paasch RK, Diettrich TG, Lytle DA, Mortensen EN, Moldenke AR, Shapiro L (2008) An aquatic insect imaging device to automate insect classification. Trans ASABE 51(6): 2217–2225
Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge
Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9: 293–300
Theodoridis S, Koutroumbas K (2006) Pattern recognition, 3rd edn. Academic Press, Edinburgh
Tirronen V, Caponio A, Haanpää T, Meissner K (2009) Multiple order gradient feature for macroinvertebrate identification using support vector machines. In: Proceedings of the adaptive and natural computing algorithms. Lecture notes in computer Science, vol 5495, pp 489–497
Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer, Berlin
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Joutsijoki, H., Juhola, M. Kernel selection in multi-class support vector machines and its consequence to the number of ties in majority voting method. Artif Intell Rev 40, 213–230 (2013). https://doi.org/10.1007/s10462-011-9281-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-011-9281-3