Yao Xiaona, Li Huijia, Wang Sili
Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China.
Key Laboratory of Knowledge Computing and Intelligent Decision, Lanzhou 730000, China.
Biomimetics (Basel). 2025 Aug 23;10(9):561. doi: 10.3390/biomimetics10090561.
High-value patents are a key indicator of new product development, the emergence of innovative technology, and a source of innovation incentives. Multiple studies have shown that patent value exhibits a significantly skewed distribution, with only about 10% of patents having high value. Identifying high-value patents from a large volume of patent data in advance has become a crucial problem that needs to be addressed urgently. However, current machine learning methods often rely on manual hyperparameter tuning, which is time-consuming and prone to suboptimal results. Existing optimization algorithms also suffer from slow convergence and local optima issues, limiting their effectiveness on complex patent datasets. In this paper, machine learning and intelligent optimization algorithms are combined to process and analyze the patent data. The Fire Hawk Optimization Algorithm (FHO) is a novel intelligence algorithm suggested in recent years, inspired by the process in nature where Fire Hawks capture prey by setting fires. This paper firstly proposes the Enhanced Fire Hawk Optimizer (EFHO), which combines four strategies, namely adaptive tent chaotic mapping, hunting prey, adding the inertial weight, and enhanced flee strategy to address the weakness of FHO development. Benchmark tests demonstrate EFHO's superior convergence speed, accuracy, and robustness across standard optimization benchmarks. As a representative real-world application, EFHO is employed to optimize Random Forest hyperparameters for high-value patent recognition. While other intelligent optimizers could be applied, EFHO effectively overcomes common issues like slow convergence and local optima trapping. Compared to other classification methods, the EFHO-optimized Random Forest achieves superior accuracy and classification stability. This study fills a research gap in effective hyperparameter tuning for patent recognition and demonstrates EFHO's practical value on real-world patent datasets.
高价值专利是新产品开发、创新技术出现的关键指标，也是创新激励的来源。多项研究表明，专利价值呈现出显著的偏态分布，只有约10%的专利具有高价值。提前从大量专利数据中识别高价值专利已成为一个亟待解决的关键问题。然而，当前的机器学习方法通常依赖于手动超参数调整，这既耗时又容易产生次优结果。现有的优化算法也存在收敛速度慢和局部最优问题，限制了它们在复杂专利数据集上的有效性。本文将机器学习和智能优化算法相结合来处理和分析专利数据。火鹰优化算法（FHO）是近年来提出的一种新型智能算法，其灵感来源于自然界中火鹰通过放火捕捉猎物的过程。本文首先提出了增强火鹰优化器（EFHO），它结合了自适应帐篷混沌映射、捕食、添加惯性权重和增强逃逸策略这四种策略来解决FHO发展中的弱点。基准测试表明，EFHO在标准优化基准测试中具有卓越的收敛速度、准确性和鲁棒性。作为一个具有代表性的实际应用，EFHO被用于优化随机森林超参数以进行高价值专利识别。虽然可以应用其他智能优化器，但EFHO有效地克服了收敛速度慢和陷入局部最优等常见问题。与其他分类方法相比，经EFHO优化的随机森林具有更高的准确性和分类稳定性。本研究填补了专利识别有效超参数调整方面的研究空白，并证明了EFHO在实际专利数据集上的实用价值。
