Machine learning approach for national innovation performance data analysis

David Bacon, Dominik Forner, Sercan Ozcan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

National innovation performance is essential for being economically competitive. The key determinants for its increase or decrease and the impact of governmental decisions or policy instruments are still not clear. Recent approaches are either limited due to qualitatively selected features or due to a small database with few observations. The aim of this paper is to propose a suitable machine learning approach for national innovation performance data analysis. We use clustering and correlation analysis, Bayesian Neural Network with Local Interpretable Model-Agnostic Explanations and BreakDown for decomposing innovation output prediction. Our results show, that the machine learning approach is appropriate to benchmark national innovation profiles, to identify key determinants on a cluster as well as on a national level whilst considering correlating features and long term effects and the impact of changes in innovation input (e.g. by governmental decision or innovation policy) on innov ation output can be predicted and herewith the increase or decrease of national innovation performance.
Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA
EditorsSlimane Hammoudi, Christoph Quix, Jorge Bernardino
PublisherSciTePress
Pages325-331
Number of pages7
Volume1
ISBN (Print)978-989-758-377-3
DOIs
Publication statusPublished - 22 Oct 2019
Event8th International Conference on Data Science, Technology and Applications - Prague, Czech Republic
Duration: 26 Jul 201928 Jul 2019

Conference

Conference8th International Conference on Data Science, Technology and Applications
Country/TerritoryCzech Republic
CityPrague
Period26/07/1928/07/19

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