Performance evaluation of college laboratories based on fusion of decision tree and BP neural network
| dc.contributor.author | Yujie, Chang | |
| dc.contributor.author | Weimin, Gao | |
| dc.contributor.author | Chelli, Karim | |
| dc.contributor.author | Muttar, Ahmed K. H. | |
| dc.date.accessioned | 2022-05-22T11:33:02Z | |
| dc.date.available | 2022-05-22T11:33:02Z | |
| dc.date.copyright | © 2021 | |
| dc.date.issued | 2022-07-01 | |
| dc.description.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. © 2021 Chang Yujie et al., published by Sciendo 2021. | |
| dc.description.sponsorship | Education Department of Hunan Province: 18C0926, 20A144 Natural Science Foundation of Hunan Province: 2018JJ2084 Huaiyin Institute of Technology Beijing Science and Technology Planning Project : S2018F9031017251 | |
| dc.identifier.citation | Yujie, C., Weimin, G., Chelli, K., & Muttar, A. K. H. (2022). Performance evaluation of college laboratories based on fusion of decision tree and BP neural network. Applied Mathematics and Nonlinear Sciences, 7(2), 1-14. https://doi.org/10.2478/amns.2022.1.00001 | |
| dc.identifier.issn | 24448656 | |
| dc.identifier.uri | https://doi.org/10.2478/amns.2022.1.00001 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12519/658 | |
| dc.language.iso | en_US | |
| dc.publisher | Sciendo | |
| dc.relation | Authors Affiliations : Yujie, C., Department Of Computer And Information Science, Hunan Institute Of Technology, No. 18, Henghua Road, Zhuhui District, HengYang, 421002, China; Weimin, G., Department Of Computer And Information Science, Hunan Institute Of Technology, No. 18, Henghua Road, Zhuhui District, HengYang, 421002, China; Chelli, K., Canadian University Dubai, United Arab Emirates; Muttar, A.K.H., Applied Science University, Bahrain | |
| dc.relation.ispartofseries | Applied Mathematics and Nonlinear Sciences Volume 7, Issue 2 | |
| dc.rights | This work is licensed under the Creative Commons Attribution 4.0 International License. | |
| dc.rights.holder | Copyright : © 2021 Chang Yujie et al., published by Sciendo 2021. | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
| dc.subject | BP neural network | |
| dc.subject | decision tree | |
| dc.subject | laboratory | |
| dc.subject | Performance evaluation | |
| dc.title | Performance evaluation of college laboratories based on fusion of decision tree and BP neural network | |
| dc.type | Article |