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An enhanced krill herd optimization technique used for classification problem

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EN
Abstrakty
EN
In this paper, this method is intended to improve the optimization of the classification problem in machine learning. The EKH as a global search optimization method, it allocates the best representation of the solution (krill individual) whereas it uses the simulated annealing (SA) to modify the generated krill individuals (each individual represents a set of bits). The test results showed that the KH outperformed other methods using the external and internal evaluation measures.
Twórcy
  • Ministry of Education, General Directorate of Vocational Education, City Baghdad, Iraq
  • Ministry of Higher Education and Scientific Research – Iraq
  • Murdoch University, PhD holder from the School of Engineering and Information Technology
Bibliografia
  • Abualigah, L.M., Khader, A.T. & Hanandeh, E.S. (2019). Modified krill herd algorithm for global numerical optimization problems. In S.K. Shandilya (eds.), Advances in nature-inspired computing and applications (pp. 205-221). Cham: Springer.
  • Mirjalili, S. & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51-67.
  • Bolaji, A.L.A., Al-Betar, M.A., Awadallah, M.A., Khader, A.T. & Abualigah, L.M. (2016). A comprehensive review: Krill Herd algorithm (KH) and its applications. Applied Soft Computing, 49, 437-446.
  • Christian, H., Agus, M.P. & Suhartono, D. (2016). Single document automatic text summarization using term frequency-inverse document frequency (TF-IDF). ComTech: Computer, Mathematics and Engineering Applications, 7(4), 285-294.
  • Forsati, R., Keikha, A. & Shamsfard, M. (2015). An improved bee colony optimization algorithm with an application to document clustering. Neurocomputing, 159, 9-26.
  • Gandomi, A.H. & Alavi, A.H. (2012). Krill herd: a new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831-4845.
  • Hofmann, E.E., Haskell, A.E., Klinck, J.M. & Lascara, C.M. (2004). Lagrangian modelling studies of Antarctic krill (Euphausia superba) swarm formation. ICES Journal of Marine Science, 61(4), 617-631.
  • Jaya, I., Aulia, I., Hardi, S.M., Tarigan, J.T. & Lydia, M.S. (2019). Scientific documents classification using support vector machine algorithm. Journal of Physics: Conference Series, 1235(1), 012082. https://doi.org/10.1088/1742-6596/1235/1/012082
  • Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L. & Brown, D. (2019). Text classification algorithms: a survey. Information, 10(4), 150. https://doi.org/10.3390/info10040150
  • Li, C.N., Shao, Y.H., Yin, W. & Liu, M.Z. (2019). Robust and sparse linear discriminant analysis via an alternating direction method of multipliers. IEEE Transactions on Neural Networks and Learning Systems, 31(3), 915-926.
  • Merendino, S. & Celebi, M.E. (2013). A simulated annealing clustering algorithm based on center perturbation using Gaussian mutation. In Ch. Boonthum-Denecke, G.M. Youngblood (eds.), Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference (pp. 456-461). Palo Alto: AAAI Press.
  • Otranto, E. (2005). The multi-chain Markov switching model. Journal of Forecasting, 24(7), 523-537.
  • Opara, K.R. & Arabas, J. (2019). Differential evolution: a survey of theoretical analyses. Swarm and Evolutionary Computation, 44, 546-558.
  • Saad, M.K. (2010). The impact of text preprocessing and term weighting on Arabic text classification. Gaza: Computer Engineering, the Islamic University.
  • Schaetti, N. (2019). Behaviors of reservoir computing models for textual documents classification. 2019 International Joint Conference on Neural Networks (IJCNN), 2019, 1-7.
  • Shah, H., Tairan, N., Mashwani, W.K., Al-Sewari, A.A., Jan, M.A. & Badshah, G. (2017). Hybrid global crossover bees algorithm for solving boolean function classification task. In International Conference on Intelligent Computing (pp. 467-478). Springer: Cham.
  • Sridharan, K. & Komarasamy, G. (2020). Sentiment classification using harmony random forest and harmony gradient boosting machine. Soft Computing, 24(10), 7451-7458.
  • Uysal, A.K. & Gunal, S. (2014). The impact of preprocessing on text classification. Information Processing & Management, 50(1), 104-112.
  • Wang, G.G., Guo, L., Gandomi, A.H., Alavi, A.H. & Duan, H. (2013). Simulated annealing-based krill herd algorithm for global optimization. Abstract and Applied Analysis, 2013, 213853. https://doi.org/10.1155/2013/213853
  • Yee, C.W. (2020). Retrieving semantically relevant documents using Latent Semantic Indexing. Yangon: Unversity of Computer Studies
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f870df69-3f25-4ab4-969f-a3281631f79b
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