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数据分析与知识发现  2019, Vol. 3 Issue (5): 117-124    DOI: 10.11925/infotech.2096-3467.2018.0674
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
专利价值评估与分类研究*——基于自组织映射支持向量机
周成(),魏红芹
东华大学旭日工商管理学院 上海 200051
Evaluating and Classifying Patent Values Based on Self-Organizing Maps and Support Vector Machine
Cheng Zhou(),Hongqin Wei
Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China
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摘要 

【目的】充分利用专利数据, 研究专利价值评估和分类问题。【方法】根据专利的价值指标, 设计基于自组织映射(SOM)-支持向量机(SVM)的专利价值评估及分类模型, 使用自组织映射方法确定专利的价值类别, 采用随机森林(RF)对价值指标进行重要性排序, 并结合包裹式特征选择方法对价值指标进行约简, 以提高SVM的分类性能。【结果】通过SOM确定的价值标签能有效反映专利价值的高低; 同时, 约简后的指标由初始的14个减少到10个, 分类准确率由76.28%提高到86.89%。【局限】对每个类别中的专利价值没有细化, 专利价值指标存在进一步约减的可能。【结论】本文方法能够为专利研发活动提供支持, 避免过度依赖专家判断。

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周成
魏红芹
关键词 专利价值评估数据聚类专利价值分类特征选择自组织映射支持向量机    
Abstract

[Objective] This paper proposes a new method for evaluating and classifying patent values. [Methods] With the help of value indicators, we designed a patent value analysis and classification system based on self-organizing maps (SOM) and support vector machine (SVM) techniques. We used the SOM to determine value categories, and then applied the random forest (RF) algorithm to rank value indictors based on their significance. Finally, we improved classification performance with the wrapped feature reduction method. [Results] The value tags determined by SOM effectively represented the patent values. Meanwhile, the value indictors were reduced from 14 to 10, and the classification accuracy was increased from 76.28% to 86.89%. [Limitations] Further refinement of patent values in each category is needed, which might reduce the patent value indicators. [Conclusions] The proposed SOM-RF-SVM method could support research and development activities as well as reduce the dependence on human factors.

Key wordsEvaluation of Patent Values    Data Clustering    Classification of Patent Values    Feature Selection    Self-Organizing Maps    Support Vector Machine
收稿日期: 2018-06-25     
基金资助:*本文系东华大学人文社会科学繁荣基金项目“互联网个性化定制用户需求多粒度模型研究”(项目编号: 108-10-0108076)的研究成果之一
引用本文:   
周成,魏红芹. 专利价值评估与分类研究*——基于自组织映射支持向量机[J]. 数据分析与知识发现, 2019, 3(5): 117-124.
Cheng Zhou,Hongqin Wei. Evaluating and Classifying Patent Values Based on Self-Organizing Maps and Support Vector Machine. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0674.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0674
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