International Journal on Advanced Science, Engineering and Information Technology, Vol. 11 (2021) No. 4, pages: 1447-1454, DOI:10.18517/ijaseit.11.4.14868

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.

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