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Falcon optimization algorithm for bayesian network structure learning

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EN
Abstrakty
EN
In machine-learning, some of the helpful scientific models during the production of a structure of knowledge are Bayesian networks. They can draw the relationships of probabilistic dependency among many variables. The score and search method is a tool that is used as a strategy for learning the structure of a Bayesian network. The authors apply the falcon optimization algorithm (FOA) to the learning structure of a Bayesian network. This paper has employed reversing, deleting, moving, and inserting to obtain the FOA for approaching the optimal solution of a structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is associated with pigeon-inspired optimization, greedy search, and simulated annealing that apply the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques by utilizing several benchmark data sets. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values).
Wydawca
Czasopismo
Rocznik
Tom
Strony
553--569
Opis fizyczny
Bibliogr. 35 poz., rys., tab.
Twórcy
  • Erbil Polytechnic University, Department of Technical Information Systems Engineering, Erbil Technical Engineering College , Erbil-Iraq
  • Lebanese French University, Department of Information Technology, College of Engineering and Computer Science, Erbil-Iraq
  • Yasar University, Software Engineering Department, Faculty of Engineering, Izmir-Turkey
Bibliografia
  • [1] Askari M.B.A., Ahsaee M.G.: Bayesian network structure learning based on cuckoo search algorithm. In: 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), pp. 127–130, 2018.
  • [2] Campos de L.M.: A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests, Journal of Machine Learning Research, vol. 7(77), pp. 2149–2187, 2006.
  • [3] Cooper G.F., Herskovits E.: A Bayesian method for the induction of probabilistic networks from data, Machine Learning, vol. 9, pp. 309–347, 1992.
  • [4] Cowie J., Oteniya L., Coles R.: Particle Swarm Optimisation for learning Bayesian Networks. In: Proceedings of the World Congress on Engineering 2007, Vol. I, 2007.
  • [5] Djan-Sampson P.O., Sahin F.: Structural learning; of Bayesian networks from complete data using the scatter search documents. In: 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), vol. 4, pp. 3619–3624, 2004.
  • [6] Fan X., Yuan C., Malone B.: Tightening Bounds for Bayesian Network Structure Learning. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 2439–2445, 2014.
  • [7] Fast A.S.: Learning The Structure of Bayesian Networks with Constraint Satisfaction, Ph.D. thesis, University of Massachusetts Amherst, Department of Computer Science, 2010. https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1174&context=open access dissertations.
  • [8] Fortier N., Sheppard J., Pillai K.G.: Bayesian abductive inference using overlapping swarm intelligence. In: 2013 IEEE Symposium on Swarm Intelligence (SIS), pp. 263–270, 2013. doi: 10.1109/SIS.2013.6615188.
  • [9] Gadekallu T.R., Khare N.: Cuckoo Search Optimized Reduction and Fuzzy Logic Classifier for Heart Disease and Diabetes Prediction, International Journal of Fuzzy System Applications (IJFSA), vol. 6(2), pp. 25–42, 2017. doi: 10.4018/IJFSA.2017040102.
  • [10] Gandomi A.H., Yang X.S., Talatahari S., Alavi A.H.: Metaheuristic Applications in Structures and Infrastructures, Elsevier, Oxford, 2013.
  • [11] He C.C., Gao X.G.: Structure Learning of Bayesian Networks Based On the LARS-MMPC Ordering Search Method. In: 2018 37th Chinese Control Conference (CCC), pp. 9000–9006, 2018. doi: 10.23919/ChiCC.2018.8483049.
  • [12] Hedenstrom A., Rosen M., ˚Akesson S., Spina F.: Flight performance during hunting excursions in Eleonora’s falcon Falco eleonorae, Journal of Experimental Biology, vol. 202(15), p. 2029–2039, 1999. doi: 10.1242/jeb.202.15.2029.
  • [13] Ji J., Wei H., Liu C.: An artificial bee colony algorithm for learning Bayesian networks, Soft Computing, vol. 17, pp. 983–994, 2012. doi: 10.1007/s00500-012-0966-6.
  • [14] Kareem S.W., Okur M.C.: Evaluation Of Bayesian Network Structure Learning. In: 2nd International Mediterranean Science and Engineering Congress, pp. 1313–1319, Cukurova University, 2017.
  • [15] Kareem S.W., Okur M.C.: Bayesian network structure learning using hybrid bee optimization and greedy search. In: 3rd International Mediterranean Science and Engineering Congress (IMSEC 2018), pp. 1–7, C¸ukurova University, 2018.
  • [16] Kareem S.W., Okur M.C.: Bayesian Network Structure Learning Based on Pigeon Inspired Optimization, International Journal of Advanced Trends in Computer Science and Engineering, vol. 8(1), pp. 131–137, 2019.
  • [17] Kareem S.W., Okur M.C.: Structure Learning of Bayesian Networks Using Elephant Swarm Water Search Algorithm, International Journal of Swarm Intelligence Research, vol. 11(2), pp. 19–30, 2020. doi: 10.4018/IJSIR.2020040102.
  • [18] Khanteymoori A.R., Olyaee M.H., Abbaszadeh O., Valian M.: A novel method for Bayesian networks structure learning based on Breeding Swarm algorithm, Soft Computing, vol. 22, pp. 3049–3060, 2018.
  • [19] Larranaga P., Poza M., Yurramendi Y., Murga R., Kuijpers C.: Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18(9), pp. 912–926, 1996. doi: 10.1109/34.537345.
  • [20] Li J., Chen J.: A Hybrid Optimization Algorithm for Bayesian Network Structure Learning Based on Database, Journal of Computers, vol. 9(12), pp. 2787–2791, 2014.
  • [21] Li S., Wang B.: A Method for Hybrid Bayesian Network Structure Learning from Massive Data Using MapReduce. In: 2017 IEEE 3rd International Conference on Big Data Security on Cloud (Bigdatasecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), pp. 272–276, 2017. doi: 10.1109/BigDataSecurity.2017.42.
  • [22] Margaritis D.: Learning Bayesian Network Model Structure from Data, Ph.D. thesis, Carnegie Mellon University, School of Computer Science, 2003. https://www.cs.cmu.edu/∼dmarg/Papers/PhD-Thesis-Margaritis.pdf.
  • [23] Mirjalili S., Mirjalili S.M., Lewis A.: Grey Wolf Optimizer, Advances in Engineering Software, vol. 69, pp. 46–61, 2014.
  • [24] Orphanou K., Thierens D., Bosman P.A.N.: Learning Bayesian Network Structures with GOMEA. In: GECCO ’18: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1007–1014, 2018.
  • [25] Rahier T., Mari´e S., Girard S., Forbes F.: Fast Bayesian Network Structure Learning using Quasi-Determinism Screening, 2019. https://hal.archives-ouvertes.fr/hal-01691217. Working paper or preprint.
  • [26] Sadeghi H.A.: Structure Learning of Bayesian Belief Networks Using Simulated Annealing Algorithm, Middle-East Journal of Scientific Research, vol. 18(9), pp. 1343–1348, 2013. doi: 10.5829/idosi.mejsr.2013.18.9.12375.
  • [27] Salama K.M., Freitas A.A.: ABC-Miner: An Ant-Based Bayesian Classification Algorithm. In: M. Dorigo, et al. (eds.), Swarm Intelligence. ANTS 2012, Lecture Notes in Computer Science, vol. 7461, Springer, 2012.
  • [28] Sencer S., Oztemel E., Torkul O., Kubat C., Taskin H., Yildiz G.: Bayesian Structural Learning with Minimum Spanning Tree Algorithm. In: The World Congress in Computer Science, Computer Engineering and Applied Computing, 2013.
  • [29] Tucker V.A.: Gliding flight: speed and acceleration of ideal falcons during diving and pull out, Journal of Experimental Biology, vol. 201(3), p. 403–414, 1998. doi: 10.1242/jeb.201.3.403.
  • [30] Tucker V.A.: Gliding flight: drag and torque of a hawk and a falcon with straight and turned heads, and a lower value for the parasite drag coefficient, Journal of Experimental Biology, vol. 203(24), p. 3733–3744, 2000. doi: 10.1242/jeb.203.24.3733.
  • [31] Vasconcelos Segundo de E.H., Mariani V.C., Santos Coelho dos L.: Design of heat exchangers using Falcon Optimization Algorithm, Applied Thermal Engineering, vol. 156, pp. 119–144, 2019. doi: 10.1016/j.applthermaleng.2019.04.038.
  • [32] Wang J., Liu S.: Novel binary encoding water cycle algorithm for solving Bayesian network structures learning problem, Knowledge-Based Systems, vol. 150(C), pp. 95–110, 2018.
  • [33] Yang C., Ji J., Liu J., Liu J., Yin B.: Structural learning of Bayesian networks by bacterial foraging optimization, International Journal of Approximate Reasoning, vol. 69, pp. 147–167, 2016.
  • [34] Yuan C., Malone B., Wu X.: Learning Optimal Bayesian Networks Using A* Search. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp. 2186–2191, 2011.
  • [35] Zhang S.Z., Liu L.: MCMC samples selecting for online bayesian network structure learning. In: 2008 International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1762–1767, IEEE, 2008. doi: 10.1109/ICMLC.2008.4620690.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5cb3c1e0-7b3c-4cb2-8154-3bad771ebd46
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