论文标题
包装器特征选择算法，用于优化专利价值评估的指标系统
Wrapper Feature Selection Algorithm for the Optimization of an Indicator System of Patent Value Assessment
论文作者
论文摘要
有效的专利价值评估为专利过渡提供了决策支持，并促进了专利技术的实际应用。在这项工作中分析了先前的研究评估研究的局限性，并开发了基于分类器预测准确性的包装模式选择算法。在多个UCI标准数据集上进行的验证实验表明，该算法有效地降低了特征集的大小，并显着提高了分类器的预测准确性。当使用该算法来建立专利价值评估的指标系统时，系统的大小减少了，并增强了分类器的概括性能。采用顺序的前向选择以进一步降低指标集的大小，并生成最佳的专利价值评估指标系统。
Effective patent value assessment provides decision support for patent transection and promotes the practical application of patent technology. The limitations of previous research on patent value assessment were analyzed in this work, and a wrapper-mode feature selection algorithm that is based on classifier prediction accuracy was developed. Verification experiments on multiple UCI standard datasets indicated that the algorithm effectively reduced the size of the feature set and significantly enhanced the prediction accuracy of the classifier. When the algorithm was utilized to establish an indicator system of patent value assessment, the size of the system was reduced, and the generalization performance of the classifier was enhanced. Sequential forward selection was applied to further reduce the size of the indicator set and generate an optimal indicator system of patent value assessment.
