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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (5): 117-124    DOI: 10.11925/infotech.2096-3467.2018.0674
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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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[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     
Received: 25 June 2018      Published: 03 July 2019

Cite this article:

Cheng Zhou,Hongqin Wei. Evaluating and Classifying Patent Values Based on Self-Organizing Maps and Support Vector Machine. Data Analysis and Knowledge Discovery, 2019, 3(5): 117-124.

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