Abstract
The way of efficiently classifying the fence climbing, fabric cutting, wall breaking and other environment factors, is an imperative problem for fiber-optic perimeter security system. To solve this problem, a security threats classification method based on optimized incremental support vector machine is proposed. In this method the artificial bee colony algorithm is introduced to optimize the penalty factor and kernel parameter of incremental support vector machine under specified fitness function, and the optimized incremental support vector machine is used to classify the perimeter security threats. To testify the performance of the proposed method, the experiment based on UCI datasets and actual vibration signal are made. Comparing with the support vector machine optimized by other algorithms, higher classification accuracy and less time consumption is achieved by the proposed method. Therefore, the effectiveness and the engineering application value of this proposed method is testified.
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References
Mehmood, A., Patel, V.M., Damarla, T.: Discrimination of Bipeds from Quadrupeds Using Seismic Footstep Signatures. In: International Conference on Geoscience and Remote Sensing Symposium, IGARSS2010, pp. 6920–6923. IEEE Press, Munich (2012)
Hu, Y., Lixin, L., Fangchun, D., Jin, H.: ANN-based Multi Classifier for Identification of Perimeter Events. In: 4th International Symposium on Computational Intelligence and Design, ISCID2011, pp. 158–161. IEEE Press, Hangzhou (2011)
Hu, Y., Guangshun, S., Qinren, W., Shangqin, H.: Identification of Damaging Activities for Perimeter Security. In: 1st International Conference on Signal Processing Systems, ICSS2009, pp. 162–166. IEEE Press, Singapore (2009)
Tseng, P., Yun, S.: A Coordinate Gradient Descent Method for Linearly Constrained Smooth Optimization and Support Vector Machines Training. Computational Optimization and Applications 47(2), 179–206 (2010)
Lin, S., Lee, Z., Chen, S., Tseng, T.: Parameter Determination of Support Vector Machine and Feature Selection Using Simulated Annealing Approach. Applied Soft Computing 8(4), 1505–1512 (2008)
Xu, Z., Zhao, Y., Wen, X.: State Prediction of Slagging on Coal-fired Boilers based on Simulated Annealing Algorithms and Support Vector Machine. East China Electric Power 39(3), 463–467 (2011) (in Chinese)
Alwan, H.B., Kumahamud, K.R.: Optimizing Support Vector Machine Parameters Using Continuous Ant Colony Optimization. In: 7th International Conference on Computing and Convergence Technology, ICCCT 2012, pp. 164–169. IEEE Press, New Jersey (2012)
Gao, F., Pu, H., Zhai, Y., Chen, L.: Application of Support Vector Machine and Ant Colony Algorithm in Optimization of Coal Ash Fusion Temperature. In: 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011, pp. 666–672. IEEE Press, New Jersey (2011)
Batsaikhan, O., Ho, C.K., Singh, Y.P.: A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition. Journal of Computer Science 8(1), 84–95 (2008)
Long, G.: GDP Prediction by Support Vector Machine Trained with Genetic Algorithm. In: 2nd International Conference on Signal Processing Systems, ICSPS2010, pp. V3-1-V3-3. IEEE Press, New Jersey (2010)
Wang, J., Zhang, Z., Zhang, W.: Support Vector Machine based on Double-population Particle Swarm Optimization. Journal of Convergence Information Technology 8(9), 898–905 (2013)
Huang, Q.: Fuzzy Support Vector Machine Using Particle Swarm Optimization for High-tech Enterprises Financing Risk Assessment. In: 2013 International Conference on Computational and Information Sciences, ICCIS2013, pp. 670–673. IEEE Press, New Jersey (2013)
Liu, C., Wang, X., Pan, F.: Parameters Selection and Stimulation of Support Vector Machines based on Ant Colony Optimization Algorithm. Journal of Central South University: Science and Technology 39(6), 1309–1313 (2008) (in Chinese)
Karaboga, D., Basturk, B.: A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony(ABC) Algorithm. Journal of Global Optimization 39(3), 459–471 (2007)
Karaboga, D.: An Idea based on Honey Bee Swarm for Numerical Optimization. Technical Report, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)
Karaboga, D., Basturk, B.: A Comparative Study of Artificial Bee Colony Algorithm. Applied Mathematics and Computation 214(1), 108–132 (2009)
UCI Machine Learning Repository, http://archive.ics.uci.edu/ml (accessed July 21, 2009)
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Liu, L., Sun, W., Zhou, Y., Li, Y., Zheng, J., Ren, B. (2014). Security Event Classification Method for Fiber-optic Perimeter Security System Based on Optimized Incremental Support Vector Machine. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_63
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DOI: https://doi.org/10.1007/978-3-662-45643-9_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45642-2
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