Performance evaluation of college laboratories based on fusion of decision tree and BP neural network
- Autores: Chang Yujie, Gao Weimin, Karim Chebli, Ahmed Muttar
- Localización: Applied Mathematics and Nonlinear Sciences, ISSN-e 2444-8656, Vol. 7, Nº. 2, 2022, págs. 1-14
- Idioma: inglés
- DOI: 10.2478/amns.2022.1.00001
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Resumen
Performance evaluation can promote the continuous improvement of the laboratories in a college. It is necessary to takeinto account the scientific evaluation method during the process of the performance evaluation. In this paper, a performanceevaluation method based on the fusion of the decision tree and BP neural network is presented. In detail, the decision treemodel is used to select performance evaluation indexes with high weight. The BP neural network was adopted aimingto reduce the impact of assessment prediction of classification by non-core factors. First, the data were pre-processed bytrapezoidal membership function. Then, the decision tree was generated by the C4.5 algorithm to select the evaluationindexes with high weight. Then, the BP neural network was trained with as many samples as possible by evaluationindexes; it possesses experts’ experience which can be used to predict the performance evaluation results. The methodovercomes the shortages of the separate model, eliminates the disturbance of human factors and improves the accuracy ofthe 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 administratorsand laboratory managers. Also, this paper will help them to improve the management of laboratories and provide themwith decision references for constructing the laboratories
