Abstract
A new linear classifier based on minimum circum circle (CMCC) is proposed in this paper. It first calculates the minimum circum circle of samples for each class. Then the distributions of the samples can be described by these circles. The linear separating hyperplane will intersect the connecting line of the centers of circles. Consequently, the perpendicular to the connecting line of each two centers is defined as the classifier of these two classes. Moreover, some improved classifiers are proposed when the separating hyperplane is not perpendicular to the connecting line or when there are outliers in the samples. The combined classifier based on subclasses is also discussed.
In the experiments, the CMCC and its improved algorithms are compared with some other classifiers such as support vector machine, linear discriminant analysis, etc. The experimental results show that the CMCC gives a relatively good performance on both classification accuracy and time cost.
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
Chapelle, O., Scholkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2006)
Torkkola, K.: Linear Discriminant Analysis in Document Classification. In: IEEE ICDM Workshop on Text Mining (2001)
Osuna, E., Freund, R., Girosit, F.: Training support vector machines: an application to face detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 130–136 (1997)
Kolter, J.Z., Maloof, M.A.: Learning to Detect and Classify Malicious Executables in the Wild. The Journal of Machine Learning Research 7, 2721–2744 (2006)
Ng, A.Y., Jordan, M.I.: On Discriminative vs. Generative Classifiers: A comparison of logistic regression and Naive Bayes. Neural Information Processing Systems 2(14), 841–848 (2002)
Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Mullers, K.R.: Fisher discriminant analysis with kernels. In: Neural Networks for Signal Processing IX, pp. 41–48 (1999)
Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29, 103–137 (1997)
Agresti, A.: Building and applying logistic regression models. An Introduction to Categorical Data Analysis, p. 138. Wiley, Hoboken (2007)
Gallant, S.I.: Perceptron-based learning algorithms. IEEE Transactions on Neural Networks 1(2), 179–191 (1990)
Cristianini, N., Taylor, J.S.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, X., Tan, Y., He, X. (2012). A Classifier Based on Minimum Circum Circle. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-31020-1_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31019-5
Online ISBN: 978-3-642-31020-1
eBook Packages: Computer ScienceComputer Science (R0)