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A Patent Quality Classification System Using a Kernel-PCA with SVM

Authors:
Pei-Chann Chang
Jheng-Long Wu
Cheng-Chin Tsao
Meng-Hsuan Lin

Keywords: patent quality classification, self-organizing maps, support vector machine, kernel principal component analysis, solar industries.

Abstract:
Data mining (DM) approaches such as clustering and classification are employed in this paper to identify and classify the patent quality. We develop an effective and automatic patent quality classification system. First, the Self-organizing map (SOM) is used to cluster patents automatically into different quality groups with patent quality indicators instead of via expert identification. Then, the Kernel principal component analysis (kernel-PCA) is used to extract key indicator to improve classification performance. Finally, the Support vector machine (SVM) is used to build the quality classification model. The proposed classification model is applied to classify patent quality automatically in solar industries. Experimental results show that our proposed approach KPCA-SVM can improve the performance of the patent quality classification when compared with the traditional method. Another advantage is that the computational time is largely reduced.

Pages: 19 to 23

Copyright: Copyright (c) IARIA, 2015

Publication date: July 19, 2015

Published in: conference

ISSN: 2308-4499

ISBN: 978-1-61208-419-0

Location: Nice,France

Dates: from July 19, 2015 to July 24, 2015