Hybrid Structure Learning Approach for Assessing the Phosphate Laundries Impact
Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).
 Z. Wang, Why Does the Water War Thesis -Prevail? International Co-operation and Development, European Commission, 2013.
 S. Pradhan, “Water War Thesis: A Myth or A Reality? “. International Journal of Arts, Humanities and Social Science, vol 2, no. 1, pp. 12-15, 2017.
 Pearse‐Smith, S. W. “Water war’in the Mekong Basin? “. Asia Pacific Viewpoint,vol. 53, no. 2, pp. 147-162, 2012.
 S. Marzougui, A. Sdiri, & F. Rekhiss. “Heavy metals’ mobility from phosphate washing effluents discharged in the Gafsa area (southwestern Tunisia) “. Arabian Journal of Geosciences, vol. 9, no. 12, 599, 2016.
 O. Gevaert, F. De Smet, E. Kirk, B. Van Calster, T. Bourne, S. Van Huffel, Y. Moreau, D. Timmerman, B. De Moor, G. Condous, “Predicting the outcome of pregnancies of unknown location: Bayesian networks with expert prior information compared to logistic regression“, Human Reproduction, vol. 21, no. 7, pp. 1824–1831, 2006, https://doi.org/10.1093/humrep/del083
 W. Buntine, “A guide to the literature on learning probabilistic networks from data,” IEEE Trans. Knowl. Data Eng., vol. 8, no. 2, pp. 195–210, 1996.
 H. S. Sousa, F. Prieto-Castrillo, J. C. Matos, J. M. Branco, & P. B. Lourenço, “Combination of expert decision and learned based Bayesian Networks for multi-scale mechanical analysis of timber elements“. Expert Systems with Applications, vol. 93, pp. 156-168, 2018.
 V. R. Tabar,, F. Eskandari, S. Salimi, et al. “ Finding a set of candidate parents using dependency criterion for the K2 algorithm”. Pattern Recognition Letters, vol. 111, pp. 23-29, 2018.
 H. Amirkhani, M. Rahmati, P. J. Lucas, & A. Hommersom, “Exploiting experts’ knowledge for structure learning of Bayesian networks”. IEEE transactions on pattern analysis and machine intelligence, vol 39, no 11, pp. 2154-2170, 2016
 S. Aouay, S. Jamoussi,, & Y. B. Ayed, “ Particle swarm optimization based method for Bayesian Network structure learning“. In 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO) , pp. 1-6. IEEE, 2013.
 M. Scutari, C. E. Graafland & J. M. Gutiérrez,“ Who learns better Bayesian network structures: Accuracy and speed of structure learning algorithms“. International Journal of Approximate Reasoning, vol. 115, pp. 235-253, 2019.
 G.F. Cooper, E. Herskovits, “A Bayesian method for the induction of probabilistic networks form data “, Mach. Learn. Vol. 9, pp. 309–347, 1992.
 L. Huang, G. Cai, H. Yuan, & J. Chen, “A hybrid approach for identifying the structure of a Bayesian network model“. Expert Systems with Applications, vol. 131, 308-320, 2019.
 B. S. Bloom, “Taxonomy of educational objectives: The classification of educational goals“, Cognitive domain, 1956.
 H. Ltifi, E. Benmohamed, C. Kolski, M. Ben Ayed M, “Adapted visual analytics process for intelligent decision-making: Application in a medical context“, International Journal of Information Technology & Decision Making 2019, accepted paper.
 D. Pineo, and C. Ware, “Data visualization optimization via computational modeling of perception“, IEEE Trans. on Visualization and Computer Graphics, vol. 18, no. 2, pp. 309-320, 2012.
 A. Pineo, T-D Wang, W. Aigner, S. Miksch, K. Wongsuphasawat, C. Plaisant, and B. Shneiderman, “Interactive information visualization to explore and query electronic health records”, Found Trends Hum–Comput Interact, vol. 5, no. 3, pp. 207–298, 2013.
 J. Zheng, Z. Jiang, R. Chellappa, “Cross-view action recognition via transferable dictionary learning, ” IEEE Trans. Image Process, vol. 25, no. 6, , pp. 2542-2556, 2016.
 E. Benmohamed, H. Ltifi, H., M. B. Ayed, ”Using Bloom's taxonomy to enhance interactive concentric circles representation”. In 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA) (pp. 1-8). IEEE.
 E. Benmohamed. H. Ltifi, M. Benayed “A Novel Bayesian Network Structure Learning Algorithm: Best Parents-Children,” in proceeding. IEEE ISKE, the 14th International Conference on Intelligent Systems and Knowledge Engineering, 2019.
 K. Masmoudi, L. Abid, &A. Masmoudi, “Credit risk modeling using Bayesian network with a latent variable,” vol. 127, Expert Systems with Applications, pp. 157-166, 2019.