An Extension-Based Classification System of Cloud Computing Patents
Jia-Yen Huang () and
Ke-Wei Tan
Additional contact information
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
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622020500248
Access to full text is restricted to subscribers
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:19:y:2020:i:04:n:s0219622020500248
Ordering information: This journal article can be ordered from
DOI: 10.1142/S0219622020500248
Access Statistics for this article
International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi
More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().