Name
Title | ||
|---|---|---|
A patent quality analysis and classification system using self-organizing maps with support vector machine. |
Abstract | ||
|---|---|---|
The framework of SOM-KPCA-SVM patent quality classification system. An automatic patent quality analysis and classification system is developed.The self-organizing map approach is used to cluster patents published before into different quality groups.The kernel principal component analysis is used to transform nonlinear feature space to improve classification performance.The support vector machine is used to build up the patent quality classification model.A series of experiments for patent data of the thin film solar cell are conducted, and the results are very encouraging. A plethora of patents are approved by the patent officers each year and current patent systems face a solemn quandary of evaluating these patents' qualities. Traditional researchers and analyzers have fixated on developing sundry patent quality indicators only, but these indicators do not have further prognosticating power on incipient patent applications or publications. Therefore, the data mining (DM) approaches are employed in this article to identify and to classify the new patent's quality in time. An automatic patent quality analysis and classification system, namely SOM-KPCA-SVM, is developed according to patent quality indicators and characteristics, respectively. First, the self-organizing map (SOM) approach is used to cluster patents published before into different quality groups according to the patent quality indicators and defines group quality type instead of via experts. The kernel principal component analysis (KPCA) approach is used to transform nonlinear feature space in order to improve classification performance. Finally, the support vector machine (SVM) is used to build up the patent quality classification model. The proposed SOM-KPCA-SVM is applied to classify patent quality automatically in patent data of the thin film solar cell. Experimental results show that our proposed system can capture the analysis effectively compared with traditional manpower approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.asoc.2016.01.020 | Appl. Soft Comput. |
Keywords | Field | DocType |
Patent analysis,Patent quality,Data clustering,Patent quality classification,Machine learning | Data mining,Feature vector,Thin film solar cell,Support vector machine,Kernel principal component analysis,Self-organizing map,Artificial intelligence,Patent analysis,Cluster analysis,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
41 | C | 1568-4946 |
Citations | PageRank | References |
7 | 0.52 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jheng-Long Wu | 1 | 95 | 9.54 |
| Pei-Chann Chang | 2 | 1752 | 109.32 |
| Cheng-Chin Tsao | 3 | 8 | 1.89 |
| Chin-Yuan Fan | 4 | 473 | 28.27 |