<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Performance evaluation can promote the continuous improvement of the laboratories in a college. It is necessary to take into account the scientific evaluation method during the process of the performance evaluation. In this paper, a performance evaluation method based on the fusion of the decision tree and BP neural network is presented. In detail, the decision tree model is used to select performance evaluation indexes with high weight. The BP neural network was adopted aiming to reduce the impact of assessment prediction of classification by non-core factors. First, the data were pre-processed by trapezoidal membership function. Then, the decision tree was generated by the C4.5 algorithm to select the evaluation indexes with high weight. Then, the BP neural network was trained with as many samples as possible by evaluation indexes; it possesses experts’ experience which can be used to predict the performance evaluation results. The method overcomes the shortages of the separate model, eliminates the disturbance of human factors and improves the accuracy of the evaluation. Experiments show that the model is feasible and effective in performance evaluation of college laboratories. The outcomes of this work can provide a scientific evaluation method for people such as researchers, college administrators and laboratory managers. Also, this paper will help them to improve the management of laboratories and provide them with decision references for constructing the laboratories.</p>
</abstract>

Performance evaluation can promote the continuous improvement of the laboratories in a college. It is necessary to take into account the scientific evaluation method during the process of the performance evaluation. In this paper, a performance evaluation method based on the fusion of the decision tree and BP neural network is presented. In detail, the decision tree model is used to select performance evaluation indexes with high weight. The BP neural network was adopted aiming to reduce the impact of assessment prediction of classification by non-core factors. First, the data were pre-processed by trapezoidal membership function. Then, the decision tree was generated by the C4.5 algorithm to select the evaluation indexes with high weight. Then, the BP neural network was trained with as many samples as possible by evaluation indexes; it possesses experts’ experience which can be used to predict the performance evaluation results. The method overcomes the shortages of the separate model, eliminates the disturbance of human factors and improves the accuracy of the evaluation. Experiments show that the model is feasible and effective in performance evaluation of college laboratories. The outcomes of this work can provide a scientific evaluation method for people such as researchers, college administrators and laboratory managers. Also, this paper will help them to improve the management of laboratories and provide them with decision references for constructing the laboratories.

Performance evaluation of college laboratories based on fusion of decision tree and BP neural network

Department of Computer and Information Science, Hunan Institute of Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Performance evaluation can promote the continuous improvement of the laboratories in a college. It is necessary to take into account the scientific evaluation...

Performance evaluation of college laboratories based on fusion of decision tree and BP neural network

Pubblicato online: 08 apr 2022

Pagine: 1 - 14

Ricevuto: 06 gen 2021

Accettato: 29 set 2021

DOI: https://doi.org/10.2478/amns.2022.1.00001

Parole chiave
Performance evaluation, laboratory, decision tree, BP neural network

© 2021 Chang Yujie et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Performance evaluation of college laboratories based on fusion of decision tree and BP neural network

Pubblicato online: 08 apr 2022

Pagine: 1 - 14

Ricevuto: 06 gen 2021

Accettato: 29 set 2021

DOI: https://doi.org/10.2478/amns.2022.1.00001

Parole chiavePerformance evaluation, laboratory, decision tree, BP neural network

© 2021 Chang Yujie et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Performance evaluation, laboratory, decision tree, BP neural network