کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
494772 862807 2016 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A patent quality analysis and classification system using self-organizing maps with support vector machine
ترجمه فارسی عنوان
تجزیه و تحلیل کیفیت و سیستم طبقه بندی با استفاده از نقشه های خود سازماندهی با ماشین بردار پشتیبانی
کلمات کلیدی
تجزیه و تحلیل ثبت اختراع، کیفیت ثبت اختراع، خوشه بندی داده ها، طبقه بندی ثبت اختراع، فراگیری ماشین
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• 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.

The framework of SOM-KPCA-SVM patent quality classification system.Figure optionsDownload as PowerPoint slide

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 41, April 2016, Pages 305–316
نویسندگان
, , , ,