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

Abstract

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.

Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning

“…Four filling methods and three feature screening methods were used to obtain 12 datasets. Eighteen machine learning algorithms, including logistic regression [27, 28], Latent Dirichlet allocation [29], Quadratic Discriminant Analysis [30], Stochastic Gradient Descent [31], k-Nearest Neighbor [32], Decision Tree [33], Naive Bayes [34], Gaussian Naïve Bayes [35], Multinomial Naive Bayes [36], Bernoulli Naïve Bayes [37], Support Vector Machine [38], passive-aggressive [39], AdaBoost [40], bagging, Random Forest [41], Extremely Randomized Trees [42], gradient boosting [41], XGBoost [43], and ensemble learning [44], were used to train 216 models. The process of building the models was as follows:The dataset was randomly divided into a training and a test set in a ratio of 8:2.The training set data were entered into the machine learning model, and the 10-fold cross-validation method was used to continuously adjust the model parameters, so that the parameters had the largest area under the receiver operating characteristic curve (AUC) value on the training set.…”

Soft-Label for Multi-Domain Fake News Detection

“…The membership function consists of Leap-GRU, Multilayer Perceptron (MLP [35]), and Softmax function. We employ the membership function as a news multi-classification module throughout the training phase, assigning relevant category labels to each news.…”

Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models

“…In recent years, the proliferation of digital platforms and social media has provided an unprecedented opportunity to capture and analyze large-scale data related to mental health [2] [3]. Machine learning and Natural Language Processing (NLP) techniques have shown promise in detecting linguistic patterns and indicators of suicidal ideation in diverse textbased data sources, such as social media posts, online forums, and electronic health records [4], [5], [6], [7].…”