Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem

Chih-Yao Chang and Kuo-Ping Lin
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Chih-Yao Chang: Graduate Institute of Technology, Innovation & Intellectual Property Management, National Cheng Chi University, Taipei 116302, Taiwan
Kuo-Ping Lin: Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 40704, Taiwan

Mathematics, 2020, vol. 8, issue 8, 1-16

Abstract: 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.

Keywords: classification; patent infringement; support vector machine; fuzzy selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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