Abstract
In the on-line data processing it is important to detect a novelty as soon as it appears, because it may be a consequence of gross errors or sudden change in the analysed system. In this paper we present a framework of novelty detection, based on the robust neural network. To detect novel patterns we compare responses of two autoregressive neural networks. One of them is trained with a robust learning algorithm designed to remove the influence of outliers, while the other uses simple training, based on the least squares error criterion. We present also a simple and easy to use approach that adapts this technique to data streams. Experiments conducted on data containing novelty and outliers have shown promising performance of the new method, applied to analyse temporal sequences.
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References
Barreto, G.A., Aguayo, L.: Time Series Clustering for Anomaly Detection Using Competitive Neural Networks. In: Príncipe, J.C., Miikkulainen, R. (eds.) WSOM 2009. LNCS, vol. 5629, pp. 28–36. Springer, Heidelberg (2009)
Brotherton, T., Johnson, T., Chadderdon, G.: Classification and Novelty Detection using Linear Models and a Class Dependent - Elliptical Bassi Function Neural Network. In: Proc. of the International Conference on Neural Networks, Anchorage (1998)
Chen, D.S., Jain, R.C.: A robust back propagation learning algorithm for function approximation. IEEE Trans. on Neural Networks 5, 467–479 (1994)
Chu, F., Wang, Y., Zaniolo, C.: An Adaptive Learning Approach for Noisy Data Streams. In: Proc. of the 4th IEEE Int. Conf. on Data Mining, pp. 351–354 (2004)
Chuang, C., Su, S., Hsiao, C.: The Annealing Robust Backpropagation (ARBP) Learning Algorithm. IEEE Trans. on Neural Networks 11, 1067–1076 (2000)
Crook, P., Hayes, G.: A Robot Implementation of a Biologically Inspired Method for Novelty Detection. In: Proceedings of TIMR 2001, Manchester (2001)
Hagan, M.T., Menhaj, M.B.: Training Feedforward Networks with the Marquardt Algorithm. IEEE Trans. on Neural Networks 5(6), 989–993 (1994)
Hawkins, S., He, H., Williams, G.J., Baxter, R.A.: Outlier Detection Using Replicator Neural Networks. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 170–180. Springer, Heidelberg (2002)
Himberg, J., Jussi, A., Alhoniemi, E., Vesanto, J., Simula, O.: The Self-Organizing Map as a Tool in Knowledge Engineering. In: Pattern Recognition in Soft Computing Paradigm, Soft Computing, WSP, pp. 38–65 (2001)
Hodge, V.J., Austin, J.: A Survey of Outlier Detection Methodologies. Kluwer Academic Publishers, The Netherlands (2004)
Huber, P.J.: Robust Statistics. Wiley, New York (1981)
Liano, K.: Robust error measure for supervised neural network learning with outliers. IEEE Transactions on Neural Networks 7, 246–250 (1996)
Liu, J., Gader, P.: Neural Networks with Enhanced Outlier Rejection Ability for Off-line Handwritten Word Recognition. Pattern Recognition 35(10), 2061–2071 (2002)
Ma, J., Perkins, S.: Online Novelty Detection on Temporal Sequences. In: Proc. of 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D.C, pp. 613–618 (2003)
Marsland, S.: On-Line Novelty Detection Through Self-Organisation, with Application to Inspection Robotics, Ph.D. thesis, Faculty of Science and Engineering, University of Manchester, UK (2001)
Pernia-Espinoza, A.V., Ordieres-Mere, J.B., Martinez-de-Pison, F.J., Gonzalez-Marcos, A.: TAO-robust backpropagation learning algorithm. Neural Networks 18, 191–204 (2005)
Rusiecki, A.: Robust MCD-Based Backpropagation Learning Algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 154–163. Springer, Heidelberg (2008)
Rusiecki, A.: Fast Robust Learning Algorithm Dedicated to LMLS Criterion. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS, vol. 6114, pp. 96–103. Springer, Heidelberg (2010)
Seborg, D.E., et al.: WIE Process Dynamics and Control, 2nd edn. Wiley (2004)
Taylor, O., Addison, D.: Novelty Detection Using Neural Network Technology. In: Proceedings of the COMADEN Conference (2000)
Weekley, R.A., Goodrich, R.K., Cornman, L.B.: An Algorithm for Classification and Outlier Detection of Time-Series Data. Journal of Atmospheric and Oceanic Technology 27(1), 94–107 (2010)
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Rusiecki, A. (2012). Robust Neural Network for Novelty Detection on Data Streams. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_21
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DOI: https://doi.org/10.1007/978-3-642-29347-4_21
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