A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm.

Hoshang Qasim Awla; Shahab Wahhab Kareem; Amin Salih Mohammed

doi:10.9781/ijimai.2023.01.004

Authors

Hoshang Qasim Awla Soran University
Shahab Wahhab Kareem Erbil Polytechnic University
Amin Salih Mohammed Lebanese French University

DOI:

https://doi.org/10.9781/ijimai.2023.01.004

Keywords:

Bayesian Network, Optimization Search Algorithm, Search, Structure Learning

Abstract

Bayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure. Due to the vastness of the solution space, learning Bayesian network (BN) structures from data is an NP-hard problem. The score and search technique is one Bayesian Network structure learning strategy. In Bayesian network structure learning the Falcon Optimization Algorithm (FOA) is presented and evaluated by the authors. Inserting, Reversing, Moving, and Deleting, are used in the method to create the FOA for finding the best structural solution. The FOA algorithm is based on the falcon's searching technique during drought conditions. The suggested technique is compared to the score metric function of Pigeon Inspired search algorithm, Greedy Search, and Antlion optimization search algorithm. The performance of these techniques in terms of confusion matrices was further evaluated by the authors using a variety of benchmark data sets. The Falcon optimization algorithm outperforms the previous algorithms and generates higher scores and accuracy values, as evidenced by the results of our experiments.

Downloads

Download data is not yet available.

References

Ji, J., Wei, H. & Liu, C., “An Artificial Bee Colony Algorithm For Learning Bayesian Networks,” Springer-Verlag Berlin Heidelberg, 2012.

N. Fortier, J. Sheppard and K. G. Pillai, “Abductive Inference Using Overlapping Swarm Intelligence,” In IEEE Symposium On Swarm Intelligence, 2013.

H. Q. Awla, S. W. Kareem, A. S. Mohammed, ”Bayesian Network Strucutre Discovery Using Antlion Optimization Algorithm,” International Journal Of Systmatic Inovation, 2022

Sun X., Chen C., Wang L., Kang H., Shen Y., Chen Q., “A Hybrid Optimization Algorithm For Bayesian Network Structure Learning Based On Database,” Journal Of Computers, VOL. 9, 2014.

D. Margaritis, “Learning Bayesian Network Model Structure From Data,” Carnegie-Mellon University, Pittsburgh, Pa. Available As Technical Report Cmu-, 2003.

A. S. Fast, “Learning The Structure Of Bayesian Networks With Constraint Satisfaction, Massachusetts,” Phd Thesis, Department Of Computer Science, University Of Massachusetts, February 2010, 2010.

A. S. Hesar, “Structure Learning Of Bayesian Belief Networks Using Simulated Annealing Algorithm,” Middle-East Journal Of Scientific Research 18, Pp. 1343-1348, 2013.

G. F. Cooper and E. Herskovits, “A Bayesian Method For The Induction Of Probabilistic Networks From Data,” In Machine Learning, 9, 1992.

J. Cowie, L. Oteniya, R. Coles, “Particle Swarm Optimisation For Learning Bayesian Networks,” Engineering and Physical Sciences Research Council., 2007.

Salama, K.M., Freitas, A.A. (2012), “ABC-Miner: An Ant-Based Bayesian Classification Algorithm,” Lecture Notes in Computer Science, vol 7461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32650-9_2

C. -C. He and X. -G. Gao, “Structure Learning Of Bayesian Networks Based On The LARS-MMPC Ordering Search Method,” 2018 37th Chinese Control Conference (CCC), 2018, pp. 9000-9006, doi: 10.23919/ChiCC.2018.8483049.

S. Li and B. Wang, “A Method For Hybrid Bayesian Network Structure Learning From Massive Data Using Mapreduce,” 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), 2017, pp. 272-276, doi: 10.1109/BigDataSecurity.2017.42.

Dai, J., Ren, J., Du, W. Et Al., “An Improved Evolutionary ApproachBased Hybrid Algorithm For Bayesian Network Structure Learning In Dynamic Constrained Search Space,” Neural Computing & Applications, Pp. 1413-1434, 32 2020.

Sun, X.; Chen, C.; Wang, L.; Kang, H.; Shen, Y.; Chen, Q., “Hybrid Optimization Algorithm For Bayesian Network Structure Learning,” Information 10, No. 10: 294. P. https://Doi.Org/10.3390/Info10100294, 294 10 2019.

K. Shahab, And C. Mehmet, “Bayesian Network Structure Learning Using Hybrid Bee Optimization and Greedy Search,” Adana/TURKEY, 2018.

A. C. Constantinou, “Learning Bayesian Networks With The Saiyan Algorithm,” Acm Trans. Knowl. Discov. Data, P. Doi: https://Doi.Org/10.1145/3385655, 4 14 2020.

Cuicui Yang, Junzhong Ji, Jiming Liu, Jinduo Liu And Baocai Yin, “Structural Learning Of Bayesian Networks By Bacterial Foraging Optimization,”, International Journal Of Approximate Reasoning, P. 69, 2016.

P. Larraiiaga , M. Poza, “Structure Learning Of Bayesian Networks By Genetic Algorithms,” Springer-Verlag Berlin Heidelberg Gmbh, 1996.

Kalia Orphanou, Dirk Thierens, and Peter A. N. Bosman, “Learning Bayesian Network Structures With GOMEA,” Kyoto, Japan, 2018.

Kareem, S. W., & Okur, M. C. (2021)., “A Novel Method Forbayesian Networks Structure Learning Based On Breeding Swarm Algorithm,” Soft Computing, Nr. 9, 9 2018.

Jingyunwang, And Sanyangliu, “Novel Binary Encoding Water Cycle Algorithm For Solving Bayesian Network Structures Learning Problem,” Knowledge-Based Systems, Bd. 150, 2018.

Kareem, S.W., & Okur, M.C. (2019), “Bayesian Network Structure Learning Based On Pigeon Inspired Optimization,” International Journal Of Advanced Trends In Computer Science And Engineering, Bd. 8, Nr. 1.2, Pp. 131-137, 2019.

Fan, X., Yuan, C., & Malone, B.M. (2014), “Tightening Bounds For Bayesian Network Structure Learning,” In Association For The Advancement Of Artificial Intelligence, 2014.

Changheyuan, B. And Xiaojian W., “Learning Optimal Bayesian Networks Using A* Search,” In NSF Grants IIS-0953723 and EPS-0903787, 21 IJCAI., Barcelona, 2011.

Patrick O. Djan-Sampson and Ferat S., “Structural Learning; Of Bayesian Networks From Complete Data Using The Scatter Search Documents,” In IEEE International Conference On Systems, Man And Cybernetics, 2004.

M. B. A. Askari and M. G. Ahsaee, “Bayesian Network Structure Learning Based On Cuckoo Search Algorithm,” In 2018 6th Iranian Joint Congress On Fuzzy And Intelligent Systems (CFIS), Kerman, Iran, 2018.

Thibaud R., Sylvain M., Stephane G., Florence F., “Fast Bayesian Network Structure Learning Using Quasi-Determinism Screening,” HAL, Bd. 2, Pp. 14-24, 2019.

Safiye S., Ercan O., Harun T. And Orhan T., “Bayesian Structural Learning With Minimum Spanning Tree Algorithm,” In The World Congress In Computer Science, Computer Engineering And Applied Computing, 2013.

J. Cowie, L. Oteniya, R. Coles, “Particle Swarm Optimisation For Learning Bayesian Networks,” In Engineering and Physical Sciences Research Council, 2007.

L. M. D. Campos, “A Scoring Function For Learning Bayesian Networks Based On Mutual Information And Conditional Independence Tests,” Journal Of Machine Learning Research, No. 7, Pp. 2149-2187, 2006.

Zhang S., Liu L., “Mcmc Samples Selecting For Online Bayesian Network Structure Learning.” Kunming, 2008.

D. Heckerman, D. Geiger, And D. M. Chickering, “Learning Bayesian Networks: The Combination Of Knowledge And Statistical Data,” Machine Learning, Pp. 197-243, 1995.

Thippa Reddy, G., & Khare, N., “FFBAT-Optimized Rule Based Fuzzy Logic Classifier For Diabetes,” International Journal Of Engineering Research In Africa Trans Tech Publications, Pp. 137-152, 2016.

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), Pp. 25-42, 2017.

Ding, Q., & Xu, X., “Improved GWO Algorithm for UAV Path Planning on Crop Pest Monitoring,” International Journal Of Interactive Multimedia And Artificial Intelligence, Vol. 7 (Special Issue on Multimedia Streaming and Processing in Internet of Things with Edge Intelligence), 30-39, 2022.

Seyedali M., Seyed M. M., Andrew L., “A Grey Wolf Optimizer,” Advances In Engineering Software, Bd. 69, Pp. 46-61, 2014.

Verma, K.K.; Singh, B.M., “Deep Multi-Model Fusion for Human Activity Recognition Using Evolutionary Algorithms,” International Journal Interactive Multimedia Artificial Intelligence 2021, 7, 44–58.

Segundo H. De V., Cocco V., Dos Santos Coelho L., “Design Of Heat Exchangers Using Falcon Optimization Algorithm,” Applied Thermal Engineering, Pp. 119-144, 2019.

V. Tucker, “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 Experimental Biology 203, P. 3733–3744., 2000.

V. Tucker, “Gliding Flight: Speed and Acceleration Of Ideal Falcons During Diving And Pull Out,” Journal Experimental Biology 201, P. 403– 414., 1998.

Anders H. Mikael R., Susanne Å., And Fernando S., “Flight Performance During Hunting Excursions In Eleonora’s Falcon Falco Eleonorae,” The Journal Of Experimental Biology, P. 2029–2039, 1999.

D. Dekker, “Hunting Tactics Of Peregrines And Other Falcons, Wageningen,” The Netherlands Wageningen University, 2009.

R. Nagarajan, M. Scutari And S. Lèbre, “Bayesian Networks In R With Applications In Systems Biology,” New York: Springer, 2013.