An Extension-Based Classification System of Cloud Computing Patents

Jia-Yen Huang () and Ke-Wei Tan
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Jia-Yen Huang: Department of Information Management, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung City 41170, Taiwan, Republic of China
Ke-Wei Tan: Department of Information Management, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung City 41170, Taiwan, Republic of China

International Journal of Information Technology & Decision Making (IJITDM), 2020, vol. 19, issue 04, 1149-1172

Abstract: Owing to the large number of professional glossaries and unknown patent classification, analysts usually fail to collect and analyze patents efficiently. One solution to this problem is to conduct patent analysis using a patent classification system. However, in a corpus such as cloud patents, many keywords are common among different classes, making it difficult to classify the unknown class documents using the machine learning techniques proposed by previous studies. To remedy this problem, this study aims to establish an efficient classification system with a special focus on features extraction and application of extension theory. We first propose a compound method to determine the features, and then, we propose an extension-based classification method to develop an efficient patent classification system. Using cloud computing patents as the database, the experimental results show that our proposed scheme can outperform the classification quality of the traditional classifiers.

Keywords: Cloud computing; patent classification; feature selection; extension theory; classifiers; gray relational analysis (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0219622020500248

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