Analyzing Domestic Violence through Exploratory Data Analysis and Explainable Ensemble Learning Insights

• URL: http://arxiv.org/abs/2403.15594v2
• Date: Tue, 21 Jan 2025 22:57:04 GMT
• Title: Analyzing Domestic Violence through Exploratory Data Analysis and Explainable Ensemble Learning Insights
• Authors: Md Abrar Jahin, Saleh Akram Naife, Fatema Tuj Johora Lima, M. F. Mridha, Jungpil Shin,
• Abstract summary: This study explores male domestic violence (MDV) for the first time, highlighting the factors that influence it. We collected data from nine major cities in Bangladesh and conducted exploratory data analysis (EDA) to understand the underlying dynamics. EDA revealed patterns such as the high prevalence of verbal abuse, the influence of financial dependency, and the role of familial and socio-economic factors in MDV.
• Score: 0.5825410941577593
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• Abstract: Domestic violence is commonly viewed as a gendered issue that primarily affects women, which tends to leave male victims largely overlooked. This study explores male domestic violence (MDV) for the first time, highlighting the factors that influence it and tackling the challenges posed by a significant categorical imbalance of 5:1 and a lack of data. We collected data from nine major cities in Bangladesh and conducted exploratory data analysis (EDA) to understand the underlying dynamics. EDA revealed patterns such as the high prevalence of verbal abuse, the influence of financial dependency, and the role of familial and socio-economic factors in MDV. To predict and analyze MDV, we implemented 10 traditional machine learning (ML) models, three deep learning models, and two ensemble models, including stacking and hybrid approaches. We propose a stacking ensemble model with ANN and CatBoost as base classifiers and Logistic Regression as the meta-model, which demonstrated the best performance, achieving 95% accuracy, a 99.29% AUC, and balanced metrics across evaluation criteria. Model-specific feature importance analysis of the base classifiers identified key features influencing their individual decision-making. Model-agnostic explainable AI techniques, SHAP and LIME, provided local and global insights into the decision-making processes of the proposed model, enhancing transparency and interpretability. Additionally, statistical validation using paired t-tests with 10-fold cross-validation and Bonferroni correction (alpha = 0.0036) confirmed the superior performance of our proposed model over alternatives.

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