2026-10-14T07:47:44Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33810139 2026-09-15T17:28:18Z category_146 category_272 category_734 category_931 category_811 portal_5 item_type_3 month_year_09_2026

<p>Search strategy.</p> Ming Lei (161082) Yin Yu (3139023) Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified proposed framework offers new automated framework achieve high sensitivity technologies often appear severe class imbalance low feature discriminability catboost &# 8217 sabo search agents experimental validation using catboost model optimized standard catboost feature importance combines catboost breakthrough technologies technology forecasting technology analysis subtle indicators strategic r strategic management stage breakthroughs model interpretation major challenge key hyperparameters interpretable tool incremental innovations greatly outnumbered class f1 breakthrough identification based models ambiguous indicators <div><p>Early identification of breakthrough technologies is essential for strategic R&D planning, but it remains a major challenge in technology analysis and strategic management. 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. Experimental validation using the Derwent Innovation patent data set shows that SABO-tuned CatBoost significantly outperforms both standard CatBoost and other optimizer-based models in F1-score for breakthrough identification. Model interpretation through feature importance and SHAP analysis identifies discriminative indicators for early-stage breakthroughs. The proposed framework offers an effective and interpretable tool for technology forecasting under these constraints, supporting early opportunity discovery and data-driven innovation strategy.</p></div> 2026-09-15T17:28:06Z Dataset Dataset 10.1371/journal.pone.0356903.t003 https://figshare.com/articles/dataset/_p_Search_strategy_p_/33810139 CC BY 4.0