Enhancing Multi-Output Classification Performance with Hybrid Models and Ensemble Learning Techniques

The study outlines a detailed approach for improving predictive model performance via sophisticated data preparation, feature extraction, and ensemble learning methods. The suggested hybrid ANN + XGBOOST approach concurrently addresses several classification jobs, effectively managing multi-class and multi-target issues. An ensemble learning strategy is employed for model enhancement by training numerous hybrid models that integrate ANN and XGBoost, with predictions aggregated through averaging to increase overall accuracy. This hybrid model's performance is evaluated against conventional classifiers, including Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). A bar chart illustrates the comparative accuracy of different models, highlighting the benefits of the hybrid strategy in attaining superior classification accuracy. This methodology provides a strong and scalable solution for addressing intricate categorization challenges across multiple domains.