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Swarm Intelligence in Semi-Supervised Classification
Abstract
This Paper represents a literature review of Swarm intelligence algorithm in the area of semi-supervised classification. There are many research papers for applying swarm intelligence algorithms in the area of machine learning. Some algorithms of SI are applied in the area of ML either solely or hybrid with other ML algorithms. SI algorithms are also used for tuning parameters of ML algorithm, or as a backbone for ML algorithms. This paper introduces a brief literature review for applying swarm intelligence algorithms in the field of semi-supervised learning.
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References
C. C. Aggarwal, “An Introduction to Data Classification,” in Data Classification: Algorithms and Applications, C. C. Aggarwal, Ed. CRC Press, 2015, pp. 1–36.
S. J. Pan and Q. Yang, “A survey on transfer learning,” Knowl. Data Eng. IEEE Trans., vol. 22, no. 10, pp. 1345–1359, 2010.
S. J. Pan and Q. Yang, “A survey on transfer learning,” Knowl. Data Eng. IEEE Trans., vol. 22, no. 10, pp. 1345–1359, 2010.
J. Blitzer, M. Dredze, and F. Pereira, “Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification,” in ACL, 2007, vol. 7, pp. 440–447.
V. Jayaram, M. Alamgir, Y. Altun, B. Scholkopf, and M. Grosse-Wentrup, “Transfer Learning in Brain-Computer Interfaces AbstractuFFFDThe performance of brain-computer interfaces (BCIs) improves with the amount of avail,” Comput. Intell. Mag. IEEE, vol. 11, no. 1, pp. 20–31, 2016.
F. Delsuc, “Army ants trapped by their evolutionary history,” PLoS Biol., vol. 1, no. 2, p. e37, 2003.
D. Simon, Evolutionary optimization algorithms: biologically-inspired and population-based approaches to computer intelligence. Hoboken, NJ: Wiley, 2013.
T. R. Schultz, “In search of ant ancestors,” Proc. Natl. Acad. Sci., vol. 97, no. 26, pp. 14028–14029, 2000.
S. Kiranyaz, T. Ince, and M. Gabbouj, Multidimensional particle swarm optimization for machine learning and pattern recognition. Springer, 2014.
C. Blum and X. Li, “Swarm Intelligence in Optimization,” in Swarm Intelligence: Introduction and Applications, C. Blum and D. Merkle, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 43–85.
A. P. Engelbrecht, Computational intelligence: an introduction, 2nd Editio. John Wiley & Sons, 2007.
D. Floreano and C. Mattiussi, Bio-inspired artificial intelligence: theories, methods, and technologies. MIT press, 2008.
S. Alam, G. Dobbie, Y. S. Koh, P. Riddle, and S. U. Rehman, “Research on particle swarm optimization based clustering: a systematic review of literature and techniques,” Swarm Evol. Comput., vol. 17, pp. 1–13, 2014.
X.-S. Yang and X. He, “Swarm Intelligence and Evolutionary Computation: Overview and Analysis,” in Recent Advances in Swarm Intelligence and Evolutionary Computation, Springer, 2015, pp. 1–23.
A. Colorni, M. Dorigo, and V. Maniezzo, “Distributed optimization by ant colonies,” in Proceedings of the first European conference on artificial life, 1991, vol. 142, pp. 134–142.
W. J. Gutjahr, “A Graph-based Ant System and its convergence,” Futur. Gener. Comput. Syst., vol. 16, no. 8, pp. 873–888, Jun. 2000.
X.-S. Yang and M. Karamanoglu, “Swarm Intelligence and Bio-Inspired Computation: An Overview,” Swarm Intell. Bio-Inspired Comput. Theory Appl., p. 1, 2013.
M. Broilo, P. Rocca, and F. G. B. De Natale, “Content-based image retrieval by a semi-supervised particle swarm optimization,” in Multimedia Signal Processing, 2008 IEEE 10th Workshop on, 2008, pp. 666–671.
A. Halder, S. Ghosh, and A. Ghosh, “Aggregation pheromone metaphor for semi-supervised classification,” Pattern Recognit., vol. 46, no. 8, pp. 2239–2248, 2013.
A. Halder, S. Ghosh, and A. Ghosh, “Ant based semi-supervised classification,” in Swarm Intelligence, Springer, 2010, pp. 376–383.
S. Cheng, Y. Shi, and Q. Qin, “Particle swarm optimization based semi-supervised learning on Chinese text categorization,” in Evolutionary Computation (CEC), 2012 IEEE Congress on, 2012, pp. 1–8.
X. Xu, L. Lu, P. He, Y. Ma, Q. Chen, and L. Chen, “Semi-supervised Classification with Multiple Ants Maximal Spanning Tree,” in Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on, 2013, vol. 2, pp. 315–320.
P. He, L. Lu, X. Xu, H. Qian, W. Zhang, and Y. Ju, “Evolutionary semi-supervised learning with swarm intelligence,” in Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014, pp. 1343–1350.
J. Albinati, S. E. L. Oliveira, F. E. B. Otero, and G. L. Pappa, “An ant colony-based semi-supervised approach for learning classification rules,” Swarm Intell., vol. 9, no. 4, pp. 315–341, 2015.
S. S. Azab, M. F. A. Hady, and H. A. Hefny, “Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization,” Int. J. Comput. Appl., vol. 160, no. 3, pp. 39–44, Feb. 2017.
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