<p>Search strategy.</p>

In their early stages, these technologies often appear in patent data with weak and ambiguous indicators and are greatly outnumbered by incremental innovations. This results in low feature discriminability and severe class imbalance.

To address these challenges, we present a new automated framework that combines CatBoost with the SABO heuristic optimization algorithm. In this approach, CatBoost’s key hyperparameters are treated as SABO search agents and are optimized to improve the minority-class F1-score using stratified cross-validation. This approach directs the model to achieve high sensitivity and robustness, given the imbalanced data and subtle indicators.