This study develops a governance-aligned framework for probability of default modeling under class imbalance, combining domain-informed feature engineering, resampling, ensemble learning and ...

Data imbalance occurs when class distributions in a dataset are significantly skewed. This fundamental challenge in machine learning can severely impact model performance, as algorithms tend to be ...

Abstract: University-industry collaboration has emerged as a critical driver of innovation and economic growth. However, predicting the outcomes of these collaborations remains methodologically ...

Abstract: This paper employs SMOTE oversampling, PSO-XGBoost, TPE-decision tree multi-output regression, and greedy algorithms to investigate feature processing, category identification, ratio ...

Software projects frequently miss deadlines. By analysing historical task data — such as estimated vs actual completion times, bug counts, and developer workload — we can train a classifier to flag at ...