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An anomaly detection approach based on hybrid differential evolution and K-means clustering in crowd intelligence
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Artificial intelligence is gradually penetrating into human society. In the network era, the interaction between human and artificial intelligence, even between artificial intelligence, becomes more and more complex. Therefore, it is necessary to describe and intervene the evolution of crowd intelligence network dynamically. This paper aims to detect the abnormal agents at the early stage of intelligent evolution.
In this paper, differential evolution (DE) and K-means clustering are used to detect the crowd intelligence with abnormal evolutionary trend.
This study abstracts the evolution process of crowd intelligence into the solution process of DE and use K-means clustering to identify individuals who are not conducive to evolution in the early stage of intelligent evolution.
Experiments show that the method we proposed are able to find out individual intelligence without evolutionary trend as early as possible, even in the complex crowd intelligent interactive environment of practical application. As a result, it can avoid the waste of time and computing resources.
In this paper, DE and K-means clustering are combined to analyze the evolution of crowd intelligent interaction.
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An anomaly detection approach based on hybrid differential evolution and K-means clustering in crowd intelligence
)
Abstract
Artificial intelligence is gradually penetrating into human society. In the network era, the interaction between human and artificial intelligence, even between artificial intelligence, becomes more and more complex. Therefore, it is necessary to describe and intervene the evolution of crowd intelligence network dynamically. This paper aims to detect the abnormal agents at the early stage of intelligent evolution.
In this paper, differential evolution (DE) and K-means clustering are used to detect the crowd intelligence with abnormal evolutionary trend.
This study abstracts the evolution process of crowd intelligence into the solution process of DE and use K-means clustering to identify individuals who are not conducive to evolution in the early stage of intelligent evolution.
Experiments show that the method we proposed are able to find out individual intelligence without evolutionary trend as early as possible, even in the complex crowd intelligent interactive environment of practical application. As a result, it can avoid the waste of time and computing resources.
In this paper, DE and K-means clustering are combined to analyze the evolution of crowd intelligent interaction.
References(20)
Anita, W.W., Christopher, F.C., Alex, P., Nada, H. and Thomas, W.M. (2010), “Evidence for a collective intelligence factor in the performance of human groups”, Science, Vol. 330 No. 6004, pp. 686-688.
Ardia, D., Boudt, K. and Carl, P. (2011), “Differential evolution with DEoptim: an application to non-convex portfolio optimization”, The R Journal, Vol. 3 No. 1, pp. 27-34.
Bergstra, J. and Bengio, Y. (2012), “Random search for hyper-parameter optimization”, Journal of Machine Learning Research, Vol. 13 No. 1, pp. 281-305.
Cai, H.R., Chung, C.Y. and Wong, K.P. (2008), “Application of differential evolution algorithm for transient stability constrained optimal power flow”, IEEE Transactions on Power Systems, Vol. 23 No. 2, pp. 719-728.
Chauhan, N., Ravi, V. and Chandra, D.K. (2010), “Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks”, Expert Systems with Applications, Vol. 36 No. 4, pp. 7659-7665.
David, L.D. and José, H. (2012), “IQ tests are not for machines”, Yet. Intelligence, Vol. 40 No. 2, pp. 77-81.
Deb, K., Pratap, A. and Agarwal, S. (2002), “A fast and elitist multi-objective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, Vol. 6 No. 2, pp. 182-197.
Dirac, P.A.M. (1926), “On the theory of quantum mechanics”, Proceedings Mathematical Physical and Engineering Sciences, Vol. 112 No. 762, pp. 661-667.
Estevez, P.A., Tesmer, M. and Perez, C.A. (2009), “Normalized mutual information feature selection”, IEEE Transactions on Neural Networks, Vol. 20 No. 2, pp. 189-201.
Fu, Y., Ding, M. and Zhou, C. (2012), “Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV”, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Vol. 42 No. 2, pp. 511-526.
Huang, J.Z., Ng, M.K. and Rong, H. (2005), “Automated variable weighting in K-means type clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27 No. 5, pp. 657-668.
Jain, A., Murty, M. and Flynn, P. (1999), “Data clustering: a review”, ACM Computing Surveys, Vol. 31 No. 3, pp. 264-323.
Ji, W., Liu, J., Pan, Z., Xu, J., Liang, B. and Chen, Y. (2018), “Quality-time-complexity crowd intelligence measurement”, International Journal of Crowd Science, Vol. 2 No. 1, pp. 18-26.
Kitayama, S., Arakawa, M. and Yamazaki, K. (2011), “Differential evolution as the global optimization technique and its application to structural optimization”, Applied Soft Computing, Vol. 11 No. 4, pp. 3792-3803.
Krishna, K. and Murty, M.N. (1999), “Genetic K-means algorithm”, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), Vol. 29 No. 3, pp. 433-439.
McKay, M.R., Smith, P.J. and Suraweera, H.A. (2008), “On the mutual information distribution of DM-based spatial multiplexing: exact variance and outage approximation”,IEEE Transactions on Information Theory, Vol. 54 No. 7, pp. 3260-3278.
Selim, S.Z. and Ismail, M.A. (1984), “K-Means type algorithms: a generalized convergence theorem and characterization of local optimality”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 6 No. 1, pp. 81-87.
Sho, S., Odanaka, S. and Hiroki, A. (2016), “A simulation study of short channel effects with a QET model based on Fermi-Dirac statistics and non-parabolicity for high-mobility MOSFETs”, Journal of Computational Electronics, Vol. 15 No. 1, pp. 76-83.
Storn, R. and Price, K. (1997), “Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, Vol. 11 No. 4, pp. 341-359.
Zhou, A., Qu, B., Li, H., Zhao, S. and Zhao, Q. (2011), “Multiobjective evolutionary algorithms: a survey of the state of the art”, Swarm and Evolutionary Computation, Vol. 1 No. 1, pp. 32-49.
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Acknowledgements
This work is supported by the National Key R&D Program of China (2017YFB1400100), and the Beijing Natural Science Foundation (4202072).
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Jianran Liu, Bing Liang and Wen Ji. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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