文献类型：期刊文献

英文题名：Mining technology hot spots in the 3D printing industry for technology strategic planning based on MRCAI

作者：Li, Shugang[1];Zhu, Lirong[1];Zhu, Boyi[1];Wang, Ru[1];Zheng, Lingling[1];Yu, Zhaoxu[2];Lu, Hanyu[1]

机构：[1]Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China;[2]East China Univ Sci & Technol, Dept Automat, Shanghai, Peoples R China

年份：2020

卷号：39

期号：5

起止页码：7135

外文期刊名：JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

收录：;EI(收录号：20204909575794);WOS:【SSCI(收录号:WOS:000595520600091)，SCI-EXPANDED(收录号:WOS:000595520600091)】；

基金：This work was supported by the Chinese National Natural Science Foundation (No. 71871135).

语种：英文

外文关键词：Technology hot spots; outlier samples; robust CAI; 3D printing; technology attention matrix

摘要：3D printing is the important part of the emerging industry, and the accurate prediction of technology hot spots (THS) in the 3D printing industry is crucial for the strategic technology planning. The patents of the THS are always in the minority and have outlier characteristics, so the existing single and rigid models cannot accurately and robustly predict the THS. In order to make up for the shortcomings of the existing research, this study proposes a model for robust composite attraction indicator (MRCAI), which avoids the impact of outlier patents on prediction accuracy depending on not only extracting the patent attraction indicators (AIs) but also constructing the robust composite attraction indicator (CAI) according to the rough consensus of predicted results of CAIs with high generalization. Specifically, firstly, this study selects the patent AIs from the four dimensions of the attraction: technology group attraction, state attraction, enterprise attraction and inventor attraction. Secondly, in order to completely describe the attraction features of patent, AIs are directly and indirectly integrated into CAIs. Thirdly, we reduce the influence of outlier patents on prediction accuracy from two aspects: on the one hand, we initially select the CAIs with good generalization performance based on the prediction error fluctuation range. On the other hand, we build the robust CAIs by calculating the consensus of CAIs with high generalization performance based on the rough set. Fourthly, the 3D printing industry technology attention matrix is constructed to map the effective technology strategic planning based on predicted patent backward citation count by MRCAI in the short, medium and long term. Finally, the experimental results on 3D printing patent data show that MRCAI can effectively improve the efficiency in dealing with samples with outlier patents and has strong flexibility and robustness in predicting the THS in 3D printing industry.