Abstract
Classification is supervised learning that uses labeled data to assign objects to classes. We distinguish false positive and false negative errors and define numerous indicators to quantify classifier performance. Pairs of indicators are considered to assess classification performance.We illustrate this with the receiver operating characteristic and the precision recall diagram. Several different classifiers with specific features and drawbacks are presented in detail: the naive Bayes classifier, linear discriminant analysis, the support vector machine (SVM) using the kernel trick, nearest neighbor classifiers, learning vector quantification, and hierarchical classification using regression trees.
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References
D. W. Aha. Editorial: Lazy learning. Artificial Intelligence Review (Special Issue on Lazy Learning), 11(1–5):7–10, June 1997.
R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley, New York, 1999.
T. Bayes. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53:370–418, 1763.
J. C. Bezdek and N. R. Pal. Two soft relatives of learning vector quantization. Neural Networks, 8(5):729–743, 1995.
J. C. Bezdek, T. R. Reichherzer, G. S. Lim, and Y. Attikiouzel. Multiple-prototype classifier design. IEEE Transactions on Systems, Man, and Cybernetics C, 28(1):67–79, 1998.
L. Breiman, J. H. Friedman, R. A. Olsen, and C. J. Stone. Classification and Regression Trees. Chapman & Hall, New Work, 1984.
F. L. Chung and T. Lee. Fuzzy learning vector quantization. In IEEE International Joint Conference on Neural Networks, volume 3, pages 2739–2743, Nagoya, October 1993.
J. Davis and M. Goadrich. The relationship between precision–recall and ROC curves. In International Conference on Machine Learning, pages 233–240, 2006.
R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. Wiley, New York, 1974.
R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7:179–188, 1936.
G. V. Kass. Significance testing in automatic interaction detection (AID). Applied Statistics, 24:178–189, 1975.
T. Kohonen. Learning vector quantization. Neural Networks, 1:303, 1988.
T. Kohonen. Improved versions of learning vector quantization. In International Joint Conference on Neural Networks, volume 1, pages 545–550, San Diego, June 1990.
J. Mercer. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society A, 209:415–446, 1909.
J. Neyman and E. S. Pearson. Interpretation of certain test criteria for purposes of statistical inference, part I. Joint Statistical Papers, Cambridge University Press, pages 1–66, 1967.
M. J. D. Powell. Radial basis functions for multi–variable interpolation: a review. In IMA Conference on Algorithms for Approximation of Functions and Data, pages 143–167, Shrivenham, 1985.
J. R. Quinlan. Induction on decision trees. Machine Learning, 11:81–106, 1986.
L. Rokach and O. Maimon. Data Mining with Decision Trees: Theory and Applications. Machine Perception and Artificial Intelligence. World Scientific Publishing Company, 2008.
B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, Cambridge, 2002.
G. Shakhnarovich, T. Darrell, and P. Indyk. Nearest–Neighbor Methods in Learning and Vision: Theory and Practice. Neural Information Processing. MIT Press, 2006.
S. Theodoridis and K. Koutroumbas. Pattern Recognition. Academic Press, San Diego, 4th edition, 2008.
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© 2012 Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden
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Runkler, T. (2012). Classification. In: Data Analytics. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-8348-2589-6_8
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DOI: https://doi.org/10.1007/978-3-8348-2589-6_8
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