Abstract
Support Vector Machine (SVM) has high classifying accuracy and good capabilities of fault-tolerance and generalization. The Rough Set Theory (RST) approach has the advantages on dealing with a large amount of data and eliminating redundant information. In this paper, we join SVM classifier with RST which we call the Improved Support Vector Machine (ISVM) to classify digital mammography. The experimental results show that this ISVM classifier can get 96.56% accuracy which is higher about 3.42% than 92.94% using SVM, and the error recognition rates are close to 100% averagely.
This paper is supported by National Science Foundation No. 60573096 and Gansu province Science Foundation of China No. 3ZS 051-A25-042.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vapnik, V.N.: The nature of statistical learning theory. Springer, Heidelberg (1995)
Osareh, A., Mirmehdil, M., Thomas, B., Markham, R.: Comparative Exudate Classification Using Support Vector Machines and Neural Networks. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2489, pp. 413–420. Springer, Heidelberg (2002)
Foody, G.M., Mathur, A.: A Relative Evaluation of Multiclass Image Classification by Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing 42(6), 1335–1343 (2004)
Ma, J.S., Theiler, J., Perkins, S.: Accurate On-line Support Vector Regression. Neural Computation 15(11), 2683–2703 (2003)
Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support Vector Clustering. Journal of Machine Learning Research 2(2), 125–137 (2001)
Kim, K.J.: Financial Time Series Forecasting Using Support Vector Machines. Neuro-computing 55(1), 307–319 (2003)
Dibike, Y.B., Velickov, S., Solomatine, D.: Support Vector Machines: Review and Applications in Civil Engineering. In: Proc. of the 2nd Joint Workshop on Application of AI in Civil Engineering, pp. 215–218 (2000)
Wang, L.P. (ed.): Support Vector Machines: Theory and Application. Springer, Heidelberg (2005)
Schlkopf, B., Smola, A J.: Learning with Kernel-Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)
Pawlak, Z.W.: Rough Sets. International Journal of Information and Computer Science 11(5), 341–356 (1982)
Lin, T.Y.: Introduction to the Special Issue on Rough Sets. International Journal of Approximate Reasoning 15(4), 287–289 (1996)
Wang, G.Y.: Rough Set Theory and Knowledge Acquisition. Xi’an Jiaotong University Press, Xi’an (2001)
(2006-9), http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html
Pawlak, Z.W.: Rough Sets and Intelligent Data Analysis. Information sciences (147), 1–12 (2002)
Antonie, M.-L., Zaiane, O.R., Coman, A.: Application of data mining techniques for medical image classification. In: Proc. of Second Intl. Workshop on Multimedia Data Mining (MDM/KDD’2001) in conjunction with Seventh ACM SIGKDD, San Francisco, pp. 94–101. ACM Press, New York (2001)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley, Reading (1993)
Hu, X., Cercone, N.: Data Mining Via Generalization, Discretization and Rough Set Feature Selection [J]. Knowledge and Information System: An International Journal, 1(1) (1999)
Chang.C., Lin, C. (2001) LIBSVM, http://www.csie.ntu.edu.tw/~cjlin/libsvm,2006-9
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jiang, Y., Li, Z., Zhang, L., Sun, P. (2007). An Improved SVM Classifier for Medical Image Classification. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_80
Download citation
DOI: https://doi.org/10.1007/978-3-540-73451-2_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73450-5
Online ISBN: 978-3-540-73451-2
eBook Packages: Computer ScienceComputer Science (R0)