Abstract
Anomalies are patterns in data that do not conform to a well-defined notion of normal behavior. One-class Support Vector Machines calculate a hyperplane in the feature space to distinguish anomalies, but the false positive rate is always high and parameter selection is a key issue. So, we propose a novel one-class framework for detecting anomalies, which takes the advantages of both boundary movement strategy and the effectiveness of evaluation algorithm on parameters optimization. First, we search the parameters by using a particle swarm optimization algorithm. Each particle suggests a group of parameters, the area under receiver operating characteristic curve is chosen as the fitness of the object function. Second, we improve the original decision function with a boundary movement. After the threshold has been adjusted, the final detection function will bring about a high detection rate with a lower false positive rate. Experimental results on UCI data sets show that the proposed method can achieve better performance than other one class learning schemes.
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References
Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)
Filzmoser, P., Maronna, R., Werner, M.: Outlier identification in high dimensions. Comput. Stat. Data Anal. 52(3), 1694–1711 (2008)
Scholkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. Adv. Neural Inf. Process. Syst. 12, 582–588 (2000)
Scholkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)
Tax, D.M.J., Duin, R.P.W.: Support vector domain description. Pattern Recogn. Lett. 20(11–13), 1191–1199 (1999)
Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)
Davy, M., Desobry, F., Gretton, A., Doncarli, C.: An online support vector machine for abnormal events detection. Signal Process. 86(8), 2009–2025 (2006)
Zhang, Y., Liu, X.D., Xie, F.D., Li, K.Q.: Fault classifier of rotating machinery based on weighted support vector data description. Expert Syst. Appl. 36(4), 7928–7932 (2009)
King, S.P., King, D.M., Astley, K., Tarassenko, L., Hayton, P., Utete, S.: The use of novelty detection techniques for monitoring high-integrity plant. In: Proceedings of the 2002 International Conference on Control Applications, Cancun, Mexico, vol. 1, pp. 221–226 (2002)
Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-class novelty detection for seizure analysis from intracranial eeg. J. Mach. Learn. Res. 7, 1025–1044 (2006)
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.: A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data. In: Data Mining for Security Applications, vol. 19 (2002)
Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., Srivastava, J.: A comparative study of anomaly detection schemes in network intrusion detection. In: Proceedings of Third SIAM Conference on Data Mining, San Francisco, vol. 3 (2003)
Giacinto, G., Perdisci, R., Del Rio, M., Roli, F.: Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Inf. Fusion 9(1), 69–82 (2008)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
Hassan, M.R., Hossain, M.M., Bailey, J., Ramamohanarao, K.: Improving k-nearest neighbour classification with distance functions based on receiver operating characteristics. In: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases—Part I, Antwerp, Belgium. Lecture Notes in Artificial Intelligence, vol. 5211, pp. 489–504. Springer, Heidelberg (2008)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Berlin (2000)
Muller, K.R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181 (2001)
Egan, J.P.: Signal Detection Theory and ROC Analysis. Academic Press, New York (1975)
Fawcett, T.: ROC graphs: Notes and practical considerations for data mining researchers. Tech. Rep. (2004)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. In: ACM Computing Surveys (2009)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Neural Networks, 1995. Proceedings. IEEE International Conference on, Piscataway, NJ, vol. 4 (1995)
Bazi, Y., Melgani, F.: Semisupervised pso-svm regression for biophysical parameter estimation. IEEE Trans. Geosci. Remote Sens. 45(6), 1887–1895 (2007)
Melgani, F., Bazi, Y.: Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE Trans. Information Technol. Biomed. 12(5), 667–677 (2008)
Peng, T., Zuo, W.L., He, F.L.: Svm based adaptive learning method for text classification from positive and unlabeled documents. Knowl. Inf. Syst. 16(3), 281–301 (2008)
Cao, L.J., Lee, H.P., Chong, W.K.: Modified support vector novelty detector using training data with outliers. Pattern Recogn. Lett. 24(14), 2479–2487 (2003)
Oliveira, A.L.I., Costa, F.R.G., Filho, C.O.S.: Novelty detection with constructive probabilistic neural networks. Neurocomputing 71(4–6), 1046–1053 (2008)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press, San Diego (2006)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Kohonen, T.: The self-organizing map. Neurocomputing 21(1–3), 1–6 (1998)
Jolliffe, I.: Principal Component Analysis. Springer, New York (2002)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: Yale: Rapid prototyping for complex data mining tasks. In: International Conference on Knowledge Discovery and Data Mining, pp. 935–940. Association for Computing Machinery, Philadelphia (2006)
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Tian, J., Gu, H. Anomaly detection combining one-class SVMs and particle swarm optimization algorithms. Nonlinear Dyn 61, 303–310 (2010). https://doi.org/10.1007/s11071-009-9650-5
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DOI: https://doi.org/10.1007/s11071-009-9650-5