Hybrid K-Nearest Neighbour and Particle Swarm Optimization Technique for Divorce Classification

Hayder Naser Khraibet AL-Behadili, Ku Ruhana Ku-Mahamud


Judgment is the ability to make a considered decision by an evolution of knowledge. With the increasing trend of applications of artificial intelligence in law, divorce prediction has become the centre of research. Divorce classification and divorce factors determination are two of the most important matters in societies. Developing an effective technique is essential to prevent communities from collapsing. The traditional techniques of artificial intelligence play a major role in classifying divorce cases. Feature selection is a powerful pre-processing method used for data classification problems. Most previous studies on divorce classification focused on heuristic feature selection methods to determine the main factors behind divorce. These heuristic methods are considered the greedy strategy which does not produce an optimal solution. In this research, a new hybrid swarm intelligence technique was proposed using particle swarm optimisation for feature selection and the K-nearest neighbour algorithm for classification.  Specifically, the proposed hybrid classifier can be used in real divorce applications where judges in their investigations can identify the factors that lead to the applications. For the experiment, five classifiers were used for performance analysis. The proposed technique was successfully applied and showed that the performance is better than the existing classifiers, namely naive Bayes, support vector machine, artificial neural network, repeated incremental pruning to pro-duce error reduction, and decision stump. Therefore, the proposed classification model is a more suitable technique for divorce classification than other artificial intelligence techniques.


K-nearest neighbour; particle swarm optimization; data classification; machine learning; feature selection.

Full Text:



Y. Zhang, D. Gong, Y. Hu, and W. Zhang, “Feature selection algorithm based on bare bones particle swarm optimization,†Neurocomputing, vol. 148, pp. 150–157, 2015.

H. B. Alwan and K. R. Ku-Mahamud, “Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter,†Int. J. Bio-Inspired Comput., vol. 9, no. 1, pp. 53–63, 2017.

J. L. Entrikin, “The Death of Common Law,†Harv. J. Law Public Policy, vol. 42, no. 351, pp. 1–137, 2019.

G. Hinchliffe and H. Walkington, “Cultivating the art of Judgement in Students,†Grad. Employab. Context, pp. 213–235, 2017.

Y. Feldman, “The law of good people: Challenging states ability to regulate human behavior,†Cambridge University, 2018.

K. Oladotun, “Divorce, a Debacle or a Panacea? A Rethink from Biblical Evaluation,†Olabisi Onabanjo University, 2018.

A. Heidari, H., Kimiaei, S. A., & Mashhadi, “Discovering the Factors Affecting Divorce in Early Marriages: A Systematic Qualitative Study,†J. Res. Behav. Sci., vol. 17, no. 2, pp. 314–323, 2019.

K. Donahey, “Effects of Divorce on Children: The Importance of Intervention,†BYU Undergrad. J. Psychol., vol. 13, no. 1, pp. 21–33, 2018.

H. Zhang, “The Influence of the Ongoing COVID-19 Pandemic on Family Violence in China,†J. Fam. Violence, pp. 1–11, 2020.

U. Eyo, “Divorce: Causes and Effects on Children,†Asian J. Humanit. Soc. Stud., vol. 6, no. 5, pp. 172–177, 2018.

B. Alarie, A. Niblett, and A. Yoon, “How artificial intelligence will affect the practice of law,†Univ. Tor. Law J., vol. 68, no. 1, pp. 106–124, 2018.

P. Bouletreau, M. Makaremi, B. Ibrahim, A. Louvrier, and N. Sigaux, “Artificial Intelligence: Applications in orthognathic surgery,†J. Stomatol. Oral Maxillofac. Surg., vol. 120, no. 4, pp. 347–354, 2019.

P. Sharma, H. Liu, W. Honggang, and Z. Shelley, “Securing wireless communications of connected vehicles with artificial intelligence,†in 2017 IEEE International Symposium on Technologies for Homeland Security, HST 2017, 2017, pp. 1–7.

L. Huimin, L. Yujie, C. Min, H. Kim, and S. Seiichi, “Brain Intelligence: Go Beyond Artificial Intelligence,†Mob. Networks Appl., vol. 23, no. 2, pp. 368–375, 2018.

M. Komorowski, L. Celi, O. Badawi, A. Gordon, and A. Faisal, “The intensive care AI clinician learns optimal treatment strategies for sepsis,†Nat. Med., vol. 24, no. 11, pp. 716–1720, 2018.

Ranjitha and A. Prabhu, “Improved Divorce Prediction Using Machine learning- Particle Swarm Optimization (PSO),†in 2020 International Conference for Emerging Technology (INCET), 2020, pp. 1–5.

J. Kong and C. Tianrui, “Is Your Marriage Reliable ? Divorce Analysis with Machine Learning Algorithms,†in Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence, 2020, pp. 1–4.

Y. Mustafa, A. Kemal, T. İLHAN, and K. Serhat, “Divorce Prediction Using Correlation Based Feature Selection and Artificial Neural Networks,†Nevşehir Hacı Bektaş Veli Üniversitesi SBE Derg., vol. 9, no. 1, pp. 259–273, 2019.

S. Goel, S. Roshan, R. Tyagi, and S. Agarwal, “Augur Justice: A Supervised Machine Learning Technique To Predict Outcomes Of Divorce Court Cases,†in Proceedings of the IEEE International Conference Image Information Processing, 2019, vol. 2019-Novem, pp. 280–285.

S. Sohail, S. Aziz, F. Tahir, S. Haqqui, and A. Hussain, “Implementation of machine learning algorithm on factors effecting divorce rate,†in 2018 International Conference on Engineering and Emerging Technologies (ICEET), 2018, pp. 1–5.

B. Arpino, M. Le Moglie, and L. Mencarini, “Machine-Learning techniques for family demography : An application of random forests to the analysis of divorce determinants in Germany,†Annu. Meet. PAA, no. 56, p. 32, 2018.

J. Li, G. Zhang, H. Yan, L. Yu, and T. Meng, “A Markov logic networks based method to predict judicial decisions of divorce cases,†in Proceedings - 3rd IEEE International Conference on Smart Cloud, 2018, no. 1, pp. 129–132.

B. Luo, Y. Feng, J. Xu, X. Zhang, and D. Zhao, “Learning to predict charges for criminal cases with legal basis,†in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp. 2727–2736.

M. Irfan, W. Uriawan, O. T. Kurahman, M. A. Ramdhani, and I. A. Dahlia, “Comparison of Naive Bayes and K-Nearest Neighbor methods to predict divorce issues,†in IOP Conference Series: Materials Science and Engineering, 2018, vol. 1.

A. M. Jabbar, “Design and Develop an Information system for Court Data in the Republic of Iraq by using SSRS Reports with SSAS Cubes,†Iraq J. Electr. Electron. Eng., vol. 11, no. 1, pp. 105–109, 2015.

I. E. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques, no. San Francisco, CA. Elsevier, 2005.

S. Kamruzzaman, “Rule Extraction using Artificial Neural Networks,†Arxiv Prepr. arXiv1009.4984, 2010.

X. Wu et al., “Top 10 algorithms in data mining,†Knowl. Inf. Syst., vol. 14, no. 1, pp. 1–37, 2008.

G. M. Bressan, V. A. Oliveira, E. R. Hruschka, and M. C. Nicoletti, “Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop,†Eng. Appl. Artif. Intell., vol. 22, no. 4–5, pp. 579–592, 2009.

A. A. Abdoos, P. K. Mianaei, and M. R. Ghadikolaei, “Combined VMD-SVM based feature selection method for classification of power quality events,†Appl. Soft Comput. J., vol. 38, pp. 637–646, 2016.

H. N. K. Al-behadili, “Classification Algorithms for Determining Handwritten Digit,†Iraq J. Electr. Electron. Eng., vol. 12, no. 1, pp. 96–102, 2016.

W. Cohen, “Fast effective rule inductionâ€, In Machine Learning Proceedings, 2435, 115–123, 1995.

B. Seerat and U. Qamar, “Rule Induction Using Enhanced RIPPER Algorithm for Clinical Decision Support,†in Sixth International Conference on Intelligent Control and Information Processing, 2015, vol. 33, no. November, pp. 83–91.

P. N. Tan, M. Steinbach, and Vipin Kumar, Introduction to data mining. 2006.

I. Faniqul, F. Rahatara, R. Sadikur, and B. Humayra, “Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques,†in International Symposium, ISCMM 2019, Springer, 2019, p. 154.

H. N. K. Al-Behadili, R. Sagban, and K. R. Ku-Mahamud, “Adaptive parameter control strategy for ant-miner classification algorithm,†Indones. J. Electr. Eng. Informatics, vol. 8, no. 1, pp. 149–162, 2020.

A. Gupta, A. Mohammad, A. Syed, and M. N., “A Comparative Study of Classification Algorithms using Data Mining: Crime and Accidents in Denver City the USA,†Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 7, pp. 374–381, 2016.

J. Wahid and H. F. A. Al-Mazini, “Classification of Cervical Cancer Using Ant-Miner for Medical Expertise Knowledge Management,†in Knowledge Management International Conference (KMICe), 2018.

DOI: http://dx.doi.org/10.18517/ijaseit.11.4.14868


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development