Title: Design of performance assessment and management model for regional technological innovation under the background of machine learning

Authors: Wenjing Zhang; Hanyuan Zhang; Kang Tian; Huaping Zhang

Addresses: School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, 450046, Henan, China ' School of Engineering Management and Real Estate, Henan University of Economics and Law, Zhengzhou, 450046, Henan, China ' College of Information and Management Science, Henan Agricultural University, Zhengzhou, 450046, Henan, China ' School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, 450046, Henan, China

Abstract: This study addresses the limitations and primitiveness of performance evaluation management in regional technological innovation enterprises. We propose a methodology based on the random forest algorithm to overcome these issues. The methodology involves preprocessing raw data from a technology company's project management system through data cleaning, feature selection, and feature transformation. Using the ID3 algorithm, we construct an index weight evaluation model by recursively creating a decision tree and selecting features based on information gain criteria. The refined model generates a performance evaluation total score. Experimental results demonstrate that the random forest algorithm achieves a satisfactory assessment of regional technological innovation performance, with a testing accuracy of 94.20%. These findings establish a scientific foundation for performance evaluation management, enabling enterprises to enhance accuracy and efficiency.

Keywords: performance evaluation management; management mode; random forest; decision tree; machine learning.

DOI: 10.1504/IJDMB.2024.137750

International Journal of Data Mining and Bioinformatics, 2024 Vol.28 No.2, pp.181 - 197

Received: 20 Apr 2023
Accepted: 07 Sep 2023
Published online: 04 Apr 2024 *

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