Improved Divorce Prediction Using Machine learning- Particle Swarm Optimization (PSO)

Ranjitha, P; Prabhu, Akshatha

doi:10.1109/incet49848.2020.9154081

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…They were able to reach 99% accuracy. This technique may also be utilized as evidence by family counseling professionals on a couple's emotional and psychological well-being [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

“…e top 10 absolute correlation features are examined in Figure 9. e fav_food (24), know_well (30), freedom_value (12), marriage (18), special_time (5), roles (19), harmony (11), happy (17), enjoy_travel (9), insult (36), humiliate (37), and trust (21) are the top correlated features. Question by the specialist If one of us apologizes when our discussion deteriorates, the discussion ends.…”

Section: Feature Engineeringmentioning

confidence: 99%

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Predicting Divorce Prospect Using Ensemble Learning: Support Vector Machine, Linear Model, and Neural Network

Fareed

Raza

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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A divorce is a legal step taken by married people to end their marriage. It occurs after a couple decides to no longer live together as husband and wife. Globally, the divorce rate has more than doubled from 1970 until 2008, with divorces per 1,000 married people rising from 2.6 to 5.5. Divorce occurs at a rate of 16.9 per 1,000 married women. According to the experts, over half of all marriages ends in divorce or separation in the United States. A novel ensemble learning technique based on advanced machine learning algorithms is proposed in this study. The support vector machine (SVM), passive aggressive classifier, and neural network (MLP) are applied in the context of divorce prediction. A question-based dataset is created by the field specialist. The responses to the questions provide important information about whether a marriage is likely to turn into divorce in the future. The cross-validation is applied in 5 folds, and the performance results of the evaluation metrics are examined. The accuracy score is 100%, and Receiver Operating Characteristic (ROC) curve accuracy score, recall score, the precision score, and the F1 accuracy score are close to 97% confidently. Our findings examined the key indicators for divorce and the factors that are most significant when predicting the divorce.

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“…They were able to reach 99% accuracy. This technique may also be utilized as evidence by family counseling professionals on a couple's emotional and psychological well-being [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

Section: Feature Engineeringmentioning

confidence: 99%

Predicting Divorce Prospect Using Ensemble Learning: Support Vector Machine, Linear Model, and Neural Network

Fareed

Raza

Zhao

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Divorce data from the University of California, Irvine (UCI) repository has been used by several re-searchers in their studies related to artificial intelligence techniques for classification [16], [17]. Ranjitha and Prabhu introduced the PSO as a machine-learning technique [16]. Their study featured two stages.…”

Section: Introductionmentioning

confidence: 99%

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

Al-Behadili¹,

Ku-Mahamud²

2021

International Journal on Advanced Science, Engineering and Information Technology

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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.

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A Review on Machine Learning Techniques in IoT-Based Smart Grid Applications

Goyal,

Ahmed

2024

Communications in Computer and Information Science

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Improved Divorce Prediction Using Machine learning- Particle Swarm Optimization (PSO)

Cited by 7 publications

References 29 publications

Predicting Divorce Prospect Using Ensemble Learning: Support Vector Machine, Linear Model, and Neural Network

Predicting Divorce Prospect Using Ensemble Learning: Support Vector Machine, Linear Model, and Neural Network

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

A Review on Machine Learning Techniques in IoT-Based Smart Grid Applications

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