Looking Deeper into the Factors Regulating Global Innovation with
PCA and Rough Sets

Jinhang Du, Xin Song, Zhen Wang, Sungho Park, Tianchen Shi

© 2018 Jinhang Du, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: SAR Journal. Volume 1, Issue 3, Pages 95-106, ISSN 2619-9955, DOI: 10.18421/SAR13-04, September 2018.

Received: 29 August 2018
Accepted: 17 September 2018
Published: 26 September 2018


A country’s economic and industrial progress is strongly governed by the level of its innovation. However, the conditions that influence and encourage stronger innovation trends are difficult to determine, and this is due in part to the lack of a clear consensus among diverse indicators of an economy’s innovative capacity as well as to the complex relations between such factors. This study independently analyzes a few representative indicators of innovation for various input variables considered to enable innovation and ranks and selects them based on two different analysis paradigms. One draws an overall picture of relationships and interactions between different variables and describes the position of significant countries, and the other selects a set of relevant features to extract rules typifying this multifaceted interaction. A good consensus is observed for these two analysis paradigms.

Keywords – Innovation, Global Innovation Index, Feature Selection, Principal Component Analysis, Rough Set Theory, Rules.


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