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http://dx.doi.org/10.6109/jkiice.2022.26.1.64

Resolving CTGAN-based data imbalance for commercialization of public technology  

Hwang, Chul-Hyun (Department of Software Fusion, Kyoung-Bok University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.1, 2022 , pp. 64-69 More about this Journal
Abstract
Commercialization of public technology is the transfer of government-led scientific and technological innovation and R&D results to the private sector, and is recognized as a key achievement driving economic growth. Therefore, in order to activate technology transfer, various machine learning methods are being studied to identify success factors or to match public technology with high commercialization potential and demanding companies. However, public technology commercialization data is in the form of a table and has a problem that machine learning performance is not high because it is in an imbalanced state with a large difference in success-failure ratio. In this paper, we present a method of utilizing CTGAN to resolve imbalances in public technology data in tabular form. In addition, to verify the effectiveness of the proposed method, a comparative experiment with SMOTE, a statistical approach, was performed using actual public technology commercialization data. In many experimental cases, it was confirmed that CTGAN reliably predicts public technology commercialization success cases.
Keywords
Public technology commercialization; Data Imbalance; Data Amplification; Tabular Data; CTGAN;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 K. S. Hwang, "R&S accountability and dilemma within the Korean science and technology context," Korean Public Administration Review, vol. 50, no. 2, pp. 189-213, Jun. 2016.   DOI
2 H. S. Kim and H. S. Lee, "Generative Adversarial Networks based Data Generation Framework for Overcoming Imbalanced Manufacturing Process Data," Journal of Korean Institute of Intelligent Systems, vol. 29, no. 1, pp. 1-8, Feb. 2019.   DOI
3 G. M. Grossman and E. Helpman, "Innovation and growth in the global economy," MIT Press, 1991.
4 T. H. Kwon, "What makes Korean firms transfer public technology and commercialize well? : An empirical study on public technology licensee firms," Ph. D. dissertation, Hanyang University, Seoul, Korea, 2020.
5 KISTI Institutional Repository. Finding a way for innovative growth of SMEs in data-based technology commercialization [Internet]. Available: https://repository.kisti.re.kr/handle/10580/15035.
6 KISTI Institutional Repository. KISTI's technology commercialization platform continues to spread [Internet]. Available: https://repository.kisti.re.kr/handle/10580/15243.
7 L. Xu, M. Skoularidou, A. Cuesta-Infante, and K. Veeramachaneni, "Modeling Tabular Data using Conditional GAN," in Proceeding of the 33th Conference on Neural Information Processing Systems(NeurIPS2019), Vancouver: NY, 2019.
8 H. S. Lee and S. S. Kim, "Naive Bayes Clasifier based Anomalous Propagation Echo Identification using Class Imbalanced Data," Journal of the Korea Institute of Information and Communication Engineering, vol. 20, no. 6, pp. 1063-1068, Jun. 2016.   DOI
9 D. H. Jo, S. H. Choi, S. K. Kim, and H. J. Lee, "The Effect of Public Technology Value on Technology Transfer Performance," Journal of Digital Convergence, vol. 16, no. 3, pp. 189-199, Mar. 2018.   DOI