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

[1] Someh N G, Pishvaee M S, Sadjadi S J, et al. A decision-making model for performance evaluation and profit sharing in a diagnostic laboratory network. Journal of Evaluation in Clinical Practice. 2020, 26(5), pp. 1498–1503. SomehN G PishvaeeM S SadjadiS J A decision-making model for performance evaluation and profit sharing in a diagnostic laboratory network Journal of Evaluation in Clinical Practice 2020 26 5 1498 1503 Search in Google Scholar

[2] Boura S A, Hesami S. Laboratory evaluation of the performance of asphalt mixtures containing biomass fillers. Road Materials & Pavement Design, 2019, pp. 1–14. BouraS A HesamiS Laboratory evaluation of the performance of asphalt mixtures containing biomass fillers Road Materials & Pavement Design 2019 1 14 Search in Google Scholar

[3] Daniels, Aubrey. Performance management: The behavioral approach to productivity improvement. National Productivity Review, 2006, 4(3), pp. 225–236. DanielsAubrey Performance management: The behavioral approach to productivity improvement National Productivity Review 2006 4 3 225 236 Search in Google Scholar

[4] Tucan P, Gherman B, Major K, et al. Fuzzy Logic-Based Risk Assessment of a Parallel Robot for Elbow and Wrist Rehabilitation. International Journal of Environmental Research and Public Health, 2020, 17(2), p. 654. TucanP GhermanB MajorK Fuzzy Logic-Based Risk Assessment of a Parallel Robot for Elbow and Wrist Rehabilitation International Journal of Environmental Research and Public Health 2020 17 2 654 Search in Google Scholar

[5] X. Wei, X. Luo, Q. Li, J. Zhang and Z. Xu, Online Comment-Based Hotel Quality Automatic Assessment Using Improved Fuzzy Comprehensive Evaluation and Fuzzy Cognitive Map, in IEEE Transactions on Fuzzy Systems, 2015, 23(1), pp. 72–84. WeiX. LuoX. LiQ. ZhangJ. XuZ. Online Comment-Based Hotel Quality Automatic Assessment Using Improved Fuzzy Comprehensive Evaluation and Fuzzy Cognitive Map in IEEE Transactions on Fuzzy Systems 2015 23 1 72 84 Search in Google Scholar

[6] Saaty R W. The analytic hierarchy process-what it is and how it is used. Mathematical Modelling, 1987, 9(3–5), pp. 161–176. SaatyR W The analytic hierarchy process-what it is and how it is used Mathematical Modelling 1987 9 3–5 161 176 Search in Google Scholar

[7] Dyer J S. Remarks on the Analytic Hierarchy Process. Management Science 1990, 36(3), pp. 249–258. DyerJ S Remarks on the Analytic Hierarchy Process Management Science 1990 36 3 249 258 Search in Google Scholar

[8] Mon D L. Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight. IEEE International Conference on Fuzzy Systems. IEEE, 1995. MonD L Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight IEEE International Conference on Fuzzy Systems. IEEE 1995 Search in Google Scholar

[9] Li X, Wang K, Liu L, et al. Application of the Entropy Weight and TOPSIS Method in Safety Evaluation of Coal Mines. Procedia Engineering, 2011, 26, pp. 2085–2091. LiX WangK LiuL Application of the Entropy Weight and TOPSIS Method in Safety Evaluation of Coal Mines Procedia Engineering 2011 26 2085 2091 Search in Google Scholar

[10] Athanassopoulos A D, Shale E. Assessing the Comparative Efficiency of Higher Education Institutions in the UK by the Means of Data Envelopment Analysis. Education Economics, 1997, 5(2), pp. 117–134. AthanassopoulosA D ShaleE Assessing the Comparative Efficiency of Higher Education Institutions in the UK by the Means of Data Envelopment Analysis Education Economics 1997 5 2 117 134 Search in Google Scholar

[11] Abbott M, Doucouliagos C. The efficiency of Australian universities: a data envelopment analysis. Economics of Education Review, 2003, 22(1), pp. 89–97. AbbottM DoucouliagosC The efficiency of Australian universities: a data envelopment analysis Economics of Education Review 2003 22 1 89 97 Search in Google Scholar

[12] Houck M M, Speaker P J, Fleming A S, et al. The balanced scorecard: Sustainable performance assessment for forensic laboratories. Science & Justice, 2012, 52(4), pp. 209–216. HouckM M SpeakerP J FlemingA S The balanced scorecard: Sustainable performance assessment for forensic laboratories Science & Justice 2012 52 4 209 216 Search in Google Scholar

[13] Wynder M. Chemico: Evaluating performance based on the Balanced Scorecard. Journal of Accounting Education, 2018, 28(3–4), pp. 221–236. WynderM Chemico: Evaluating performance based on the Balanced Scorecard Journal of Accounting Education 2018 28 3–4 221 236 Search in Google Scholar

[14] Safavian S R, Landgrebe D. A survey of decision tree classifier methodology. IEEE Transactions on Systems Man & Cybernetics, 2002, 21(3), pp. 660–674. SafavianS R LandgrebeD A survey of decision tree classifier methodology IEEE Transactions on Systems Man & Cybernetics 2002 21 3 660 674 Search in Google Scholar

[15] Da-ke Wu, Chun-yan Xie, Cheng-wei Ma. The BP Network Classification Leafminer-Infected Leaves Based on the Fractal Dimension. Advanced Engineering Forum, 2011, 1(6), pp. 163–167. WuDa-ke XieChun-yan MaCheng-wei The BP Network Classification Leafminer-Infected Leaves Based on the Fractal Dimension Advanced Engineering Forum 2011 1 6 163 167 Search in Google Scholar

[16] Guo Hui-mei, Zheng Cai-fen, Yang xiao-bin, et al. Fuzzy Comprehensive Evaluation for the Laboratory Performance in the University under Multi-judgments Situation. Journal of Liaocheng University, 2020, 33(1), pp. 10–15. GuoHui-mei ZhengCai-fen Yangxiao-bin Fuzzy Comprehensive Evaluation for the Laboratory Performance in the University under Multi-judgments Situation Journal of Liaocheng University 2020 33 1 10 15 Search in Google Scholar

[17] Roberti F, Oberegger U F, Lucchi E, et al. Energy retrofit and conservation of a historic building using multi-objective optimization and an analytic hierarchy process. Building Energy Efficiency, 2017. RobertiF ObereggerU F LucchiE Energy retrofit and conservation of a historic building using multi-objective optimization and an analytic hierarchy process Building Energy Efficiency 2017 Search in Google Scholar

[18] Jia X U, Pei L. Research of Teacher’s Performance Evaluation Model Based on AHP and Improved PSO-BP Neural Network. DEStech Transactions on Computer Science and Engineering, 2019, pp. 138–143. JiaX U PeiL Research of Teacher’s Performance Evaluation Model Based on AHP and Improved PSO-BP Neural Network DEStech Transactions on Computer Science and Engineering 2019 138 143 Search in Google Scholar

[19] Jing Xiaofei, Wang Wenhe, Duan Yulong, et al. Research on university Laboratory Safety State based on fuzzy comprehensive Evaluation. Journal of Highter Educaion, 2019, (13), pp. 70–72. JingXiaofei WangWenhe DuanYulong Research on university Laboratory Safety State based on fuzzy comprehensive Evaluation Journal of Highter Educaion 2019 13 70 72 Search in Google Scholar

[20] Wu Lirong, Qu Yalong, Cheng Weimin. Fuzzy Comprehensive Risk Evaluation and Cause Analysis of Laboratory in Universities. Research and Exploration in Laboratory, 2020, 39(2), pp. 300–303. LirongWu YalongQu WeiminCheng Fuzzy Comprehensive Risk Evaluation and Cause Analysis of Laboratory in Universities Research and Exploration in Laboratory 2020 39 2 300 303 Search in Google Scholar

[21] Shunling Chen, Baoling Chen and Shuang Gan, Performance Evaluaiton of Measuring Instruments Based on Fuzzy Comprehensive Evaluation. Industrial Engineering and Innovation Management, 2019, (2), pp. 7–13. ChenShunling ChenBaoling GanShuang Performance Evaluaiton of Measuring Instruments Based on Fuzzy Comprehensive Evaluation Industrial Engineering and Innovation Management 2019 2 7 13 Search in Google Scholar

[22] Li Hao, Zhou Wanhuai, Zhou Senxin. Research on Comprehensive Evaluation Method of Laboratories in Local Finance and Economics Universities. Research and Exploration in Laboratory, 2020, 39(1), pp. 279–283. LiHao ZhouWanhuai ZhouSenxin Research on Comprehensive Evaluation Method of Laboratories in Local Finance and Economics Universities Research and Exploration in Laboratory 2020 39 1 279 283 Search in Google Scholar

[23] Someh N G, Pishvaee M S, Sadjadi S J, et al. Performance assessment of medical diagnostic laboratories: A network DEA approach. Journal of Evaluation in Clinical Practice, 2019, 26(5), pp. 1504–1511. SomehN G PishvaeeM S SadjadiS J Performance assessment of medical diagnostic laboratories: A network DEA approach Journal of Evaluation in Clinical Practice 2019 26 5 1504 1511 Search in Google Scholar

[24] Weipeng Z, Qing L. Evaluation and optimization of input and output efficiency of University laboratory by data envelopment analysis. Journal of Intelligent and Fuzzy Systems, 2017, 33(5), pp. 2837–2842. WeipengZ QingL Evaluation and optimization of input and output efficiency of University laboratory by data envelopment analysis Journal of Intelligent and Fuzzy Systems 2017 33 5 2837 2842 Search in Google Scholar

[25] Lu Wenjie, Xu Na. Design on Performance Evaluation System of Science and Technology Innovation Platform. Journal of Hebei University of Science and Technology (Social Science), 2020, 20(2), pp. 25–32. WenjieLu NaXu Design on Performance Evaluation System of Science and Technology Innovation Platform Journal of Hebei University of Science and Technology (Social Science) 2020 20 2 25 32 Search in Google Scholar

[26] Mona, Anvari, Mohammad, et al. Performance assessment of decision-making units using an adaptive neural network algorithm: one period case [J]. The International Journal of Advanced Manufacturing Technology, 2010, 46(9–12), 1059–1069. MonaAnvari Mohammad Performance assessment of decision-making units using an adaptive neural network algorithm: one period case [J] The International Journal of Advanced Manufacturing Technology 2010 46 9–12 1059 1069 Search in Google Scholar

[27] Breiman L, Friedman J, Olshen R, et al. Classification and Regression Tress. Encyclopedia of Ecology, 1984, 40(3), pp. 582–588. BreimanL FriedmanJ OlshenR Classification and Regression Tress Encyclopedia of Ecology 1984 40 3 582 588 Search in Google Scholar

[28] Quinlan, J. R. Induction for Decision Thees. Machine Learning, 1986, 1(1), pp. 81–106. QuinlanJ. R. Induction for Decision Thees Machine Learning 1986 1 1 81 106 Search in Google Scholar

[29] Budiman E, Haviluddin, Dengan N, et al. Performance of Decision Tree C4.5 Algorithm in Student Academic Evaluation. Computational Science and Technology. 2018. BudimanE Haviluddin DenganN Performance of Decision Tree C4.5 Algorithm in Student Academic Evaluation Computational Science and Technology 2018 Search in Google Scholar

[30] Rumelhart, David E, Hinton, Geoffrey E, Williams, Ronald J. Learning representations by back-propagating errors. Nature, 1986, 323(6088), pp. 533–536. RumelhartDavid E HintonGeoffrey E WilliamsRonald J Learning representations by back-propagating errors Nature 1986 323 6088 533 536 Search in Google Scholar

[31] Zhang Zimin, Fan Yan Ying, et al. The university experimental technical personnel performance evaluation model based on the BP neural network. Journal of guangxi academy of sciences, 2013, 29(2), pp. 85–88. ZhangZimin Yan YingFan The university experimental technical personnel performance evaluation model based on the BP neural network Journal of guangxi academy of sciences 2013 29 2 85 88 Search in Google Scholar

[32] Shi Jie. College teaching model based on BP neural network laboratory evaluation model. Chinese education technology and equipment, 2015, (2), pp. 123–124. ShiJie College teaching model based on BP neural network laboratory evaluation model Chinese education technology and equipment 2015 2 123 124 Search in Google Scholar

[33] Li Junqing, Yan Lili, et al. Computer laboratory management evaluation index based on the BP neural network. Journal of laboratory research and exploration, 2011, 30(4), pp. 71–73. JunqingLi LiliYan Computer laboratory management evaluation index based on the BP neural network Journal of laboratory research and exploration 2011 30 4 71 73 Search in Google Scholar

[34] Lu Linrui, Wu Yiping, et al. College laboratory safety evaluation model based on BP neural network and its application. Journal of laboratory research and exploration, 2013, 32(2), pp. 214–218. LinruiLu YipingWu College laboratory safety evaluation model based on BP neural network and its application Journal of laboratory research and exploration 2013 32 2 214 218 Search in Google Scholar

[35] Yan-Fu Wang, Long-Ting Wang, Jun-Cheng Jiang, et al. Modelling ship collision risk based on the statistical analysis of historical data: A case study in Hong Kong waters. Ocean Engineering, 2020, 197(1). Yan-FuWang Long-TingWang Jun-ChengJiang Modelling ship collision risk based on the statistical analysis of historical data: A case study in Hong Kong waters Ocean Engineering 2020 197 1 Search in Google Scholar

[36] Yun Wu, Zuoxun Zeng. A rapid detection method of earthquake infrasonic wave based on decision-making tree and the BP neural network. International Journal of Information and Communication, 2019, 14(3), pp. 295–307. WuYun ZengZuoxun A rapid detection method of earthquake infrasonic wave based on decision-making tree and the BP neural network International Journal of Information and Communication 2019 14 3 295 307 Search in Google Scholar

[37] Bing Xiang Liu, Xu Dong Wu, Ying Xi Li, et al. Marketing Model Investigation of Cable Television Service Based on BP Neural Network and Decision Tree. Advanced Materials Research, 2013, 2, pp. 1757–1761. LiuBing Xiang WuXu Dong LiYing Xi Marketing Model Investigation of Cable Television Service Based on BP Neural Network and Decision Tree Advanced Materials Research 2013 2 1757 1761 Search in Google Scholar

[38] Safavian S R, Landgrebe D. A survey of decision tree classifier methodology. IEEE Transactions on Systems Man & Cybernetics, 2002, 21(3), pp. 660–674. SafavianS R LandgrebeD A survey of decision tree classifier methodology IEEE Transactions on Systems Man & Cybernetics 2002 21 3 660 674 Search in Google Scholar

[39] W. N. H. W. Mohamed, M. N. M. Salleh and A. H. Omar, A comparative study of Reduced Error Pruning method in decision tree algorithms, 2012 IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, 2012, pp. 392–397. MohamedW. H. W. SallehM. N. M. OmarA. H. A comparative study of Reduced Error Pruning method in decision tree algorithms 2012 IEEE International Conference on Control System, Computing and Engineering Penang, Malaysia 2012 392 397 Search in Google Scholar

[40] Wang S T, Chen D Y, Hou P G, et al. Determination of the Sodium Methylparaben Content Based on Spectrum Fluorescence Spectral Technology and GA-BP Neural Network [J]. Guang Pu Xue Yu Guang Pu Fen XI, 2015, 35(6), pp. 1606–1610. WangS T ChenD Y HouP G Determination of the Sodium Methylparaben Content Based on Spectrum Fluorescence Spectral Technology and GA-BP Neural Network [J] Guang Pu Xue Yu Guang Pu Fen XI 2015 35 6 1606 1610 Search in Google Scholar

[41] József Dombi, Tamás Jónás. Ranking trapezoidal fuzzy numbers using a parametric relation pair. Fuzzy Sets and Systems, 2020. DombiJózsef JónásTamás Ranking trapezoidal fuzzy numbers using a parametric relation pair Fuzzy Sets and Systems 2020 Search in Google Scholar

[42] Hecht-Nielsen, Theory of the backpropagation neural network, International 1989 Joint Conference on Neural Networks, Washington, DC, USA, vol. 1, 1989, pp. 593–605. Hecht-Nielsen Theory of the backpropagation neural network International 1989 Joint Conference on Neural Networks Washington, DC, USA 1 1989 593 605 Search in Google Scholar