A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance

Ping Yu Hsu, I. Wen Yeh, Ching Hsun Tseng, Shin Jye Lee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In accordance with the statistical analysis, the industrial performance is usually related to research and development (RD) intensity, and this factor indeed plausibly brings the biggest profit with patents and supporting products to the development of semiconductor industry. How to evaluate the completive performance of modern industries is an increasing issue, especially for the semiconductor industries in these decades. However, almost every traditional statistical model is deterred by the hypothesis of population and independent correlation among each feature, and this makes the result of typical regression model potentially lose reliability. To avoid this weakness, this article therefore applies a gradient boosting based method - XGBoost to evaluate the feature importance of semiconductor industries. In the simulation experiments, different findings revel certain information, apart from RD intensity, actually sway the gross net value in the annual financial announcement of semiconductor industries. Moreover, this article proposes another concept to evaluate the essential factor contributing the development of semiconductor industries. Instead of only focusing on the effect of RD intensity, this article also predicts the future growth rate (GR) of net value by applying the greedy search of XGBoost Regression.

Original languageEnglish
Article number9177120
Pages (from-to)156208-156218
Number of pages11
JournalIEEE Access
StatePublished - 2020


  • boosting regression
  • semiconductor industry
  • XGBoost


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