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

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

doi:10.1109/access.2020.3019332

Abstract

In accordance with the statistical analysis, the industrial performance is usually related to research and development (R&D) 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 R&D 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 R&D intensity, this article also predicts the future growth rate (GR) of net value by applying the greedy search of XGBoost Regression.

Highlights

Research and development (R&D) management is one of important issues in the research field of human resource management (HRM), and how to make proper investment in the expense of R&D management has become an increasing issue
To avoid the potential disadvantage brought by the conventional regression models, this article provides a machine learning based method to objectively evaluate the feature importance in the financial announcement of semiconductor industries
In accordance with the simulation results obtained by the proposed method, it can be positively observed that the feature importance varies in the continuous type

Summary

Introduction

Research and development (R&D) management is one of important issues in the research field of human resource management (HRM), and how to make proper investment in the expense of R&D management has become an increasing issue. Instead of conventional statistical methods, this article applies a novel regression model based on the mechanism of machine learning, XGBoost, to effectively evaluate the high-impact factors of semiconductor industries.

Results

Conclusion