Abstract
In this paper, we introduce a new method of designing Venn Machine taxonomy based on Support Vector Machines and k-means clustering for both binary and multi-class problems. We compare this algorithm to some other multi-probabilistic predictors including SVM Venn Machine with homogeneous intervals and a recently developed algorithm called Venn-ABERS predictor. These algorithms were tested on a range of real-world data sets. Experimental results are presented and discussed.
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Ayer, M., Brunk, H.D., Ewing, G.M., Reid, W.T., Silverman, E.: An empirical distribution function for sampling with incomplete information. Ann. Math. Statist. 26(4), 641–647 (1955)
Brier, G.W.: Verification of forecasts expressed in terms of probability. Monthly Weather Review 78(1), 1–3 (1950)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3), Â 27 (2011)
Forgy, E.W.: Cluster analysis of multivariate data: Efficiency vs interpretability of classifications. Biometrics 21, 768–769 (1965)
Lambrou, A., Papadopoulos, H., Nouretdinov, I., Gammerman, A.: Reliable probability estimates based on support vector machines for large multiclass datasets. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Karatzas, K., Sioutas, S. (eds.) AIAI 2012, Part II. IFIP AICT, vol. 382, pp. 182–191. Springer, Heidelberg (2012)
Lloyd, S.P.: Least squares quantization in pcm. IEEE Transactions on Information Theory 28, 129–137 (1982)
Vovk, V.: Venn predictors and isotonic regression. CoRR abs/1211.0025 (2012)
Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World. Springer-Verlag New York, Inc., Secaucus (2005)
Zadrozny, B., Elkan, C.: Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 694–699. ACM (2002)
Zhou, C., Nouretdinov, I., Luo, Z., Adamskiy, D., Randell, L., Coldham, N., Gammerman, A.: A comparison of venn machine with platt’s method in probabilistic outputs. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) EANN/AIAI 2011, Part II. IFIP AICT, vol. 364, pp. 483–490. Springer, Heidelberg (2011)
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Zhou, C., Nouretdinov, I., Luo, Z., Gammerman, A. (2014). SVM Venn Machine with k-Means Clustering. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Sioutas, S., Makris, C. (eds) Artificial Intelligence Applications and Innovations. AIAI 2014. IFIP Advances in Information and Communication Technology, vol 437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44722-2_27
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DOI: https://doi.org/10.1007/978-3-662-44722-2_27
Publisher Name: Springer, Berlin, Heidelberg
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