Classification problems are very important issues in real enterprises. In the patent infringement issue, accurate classification could help enterprises to understand court decisions to avoid patent infringement. However, the general classification method does not perform well in the patent infringement problem because there are too many complex variables. Therefore, this study attempts to develop a classification method, the support vector machine with new fuzzy selection (SVMFS), to judge the infringement of patent rights. The raw data are divided into training and testing sets. However, the data quality of the training set is not easy to evaluate. Effective data quality management requires a structural core that can support data operations. This study adopts new fuzzy selection based on membership values, which are generated from fuzzy c-means clustering, to select appropriate data to enhance the classification performance of the support vector machine (SVM). An empirical example based on the SVMFS shows that the proposed SVMFS can obtain a superior accuracy rate. Moreover, the new fuzzy selection also verifies that it can effectively select the training dataset.

中文翻译：

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开发具有模糊选择权的专利侵权问题支持向量机

分类问题是实际企业中非常重要的问题。在专利侵权问题中，准确的分类可以帮助企业理解法院的判决，从而避免专利侵权。但是，通用分类方法在专利侵权问题中表现不佳，因为复杂变量太多。因此，本研究尝试开发一种分类方法，即具有新模糊选择的支持向量机（SVMFS），以判断专利权的侵权行为。原始数据分为训练集和测试集。但是，训练集的数据质量不容易评估。有效的数据质量管理需要可以支持数据操作的结构核心。本研究采用基于隶属度的新模糊选择，这些数据是通过模糊c均值聚类生成的，以选择适当的数据来增强支持向量机（SVM）的分类性能。一个基于SVMFS的经验示例表明，所提出的SVMFS可以获得更高的准确率。此外，新的模糊选择还验证了它可以有效地选择训练数据集。