Overview on intelligent comprehensive evaluation methods

Overview on intelligent comprehensive evaluation methods

Yong Yang Chenxia SuoWeijie Hao Zhihui Zhang 

Postdoctoral Programme, Bank of Zhengzhou, Zhengzhou 450018, China

Beijing Institute of Petrochemical Technology, Beijing 102617, China

Corresponding Author Email: 
11 October 2017
26 September 2018
31 December 2018
| Citation



As the computer technology develops, intelligent methods play an increasingly wider role in social life. Intelligent methods are self-adaption and self-organization oriented; exhibit very strong robustness and obvious merits in solving qualitative and quantitative problems, as well as confirming the qualitative and uncertain issues. This paper sorts out the important theories and methods for intelligent evaluation, analyzes and defines the basic principles and models involved, and forecasts the application of intelligent methods in comprehensive evaluation.


intelligentization, comprehensive evaluation, research overview

1. Introduction
2. Measurement of Intelligentization
3. Main Intelligent Evaluation Methods
4. Outlook of Intelligent Evaluation Method

This paper was funded by three projects: BIPT-POPME; Development Research Centre of Beijing New Modern Industrial Area (2016); BIPT-ER (2014); URT2017J00120.


[1]    Liu D, Yin YX, Tu XY, Dong J. (2005). An evaluation method on intelligent control system intelligent level. Journal of Central South University Special 13-16.

[2]    Liu D, Yin YX, Tu XY. (2007). Research on generalized intelligent qualitative evaluation of intelligent system. Computer Science 34(9): 167-169.

[3]    Huang W, Nie D, Chen YJ. (2001). The main school and characteristics in AI research. Journal of Gannan Teachers College (3): 73-75.

[4]    Tu XY. (1994). Theories, methods and techniques of intelligent control, second national intelligent control expert seminar collected papers (1). Tsinghua University: 27-34.

[5]    Cai ZX, Xu G. (2005). Artificial intelligence control. Beijing: Chemical Industry Press 3-20.

[6]    Boser BE, Guyon IM, Vapnik VN. (1992). A training algorithm for optimal margin classifiers. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory 5: 144-152. https://doi.org/10.1145/130385.130401

[7]    Wang ZJ. (1998). Methods, problems and research trends of comprehensive evaluation. Journal of Management Sciences in China 1(1): 73-79.

[8]    Law R., Au N. (2000). Relationship modeling in tourism shopping: a decision rules induction approach. Tourism Management 21(3): 241-249. https://doi.org/10.1016/S0261-5177(99)00056-4

[9]    Wang Q, Wang XL. (2005). Research on text classification techniques integrated KNN and SVM. Chinese High Technology Letters 15(5): 19-24.

[10]    Sun XJ. (2011). Research on coal mine safety expert index evaluation system. Coal Economic Research (3).

[11]    Jing HF, Wang B, Yang YH, Xu Y. (2009). Category distribution-based feature selection framework. Journal of computer research and development 46(9): 1586-1593.

[12]    Yang Y, Slattery S, Ghani R. (2002). A study of approaches to hypertext categorization. Journal of Intelligent Information Systems 18(2): 219-241. https://doi.org/10.1023/A:1013685612819

[13]    Vapnil VN. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks 10(5): 988-999. https://doi.org/10.1109/72.788640

[14]    Burges CJC. (1999). Geometry and invariance in kernel based method. Advances in Kernel Methods-Support Vector Learning, Cambridge: MIT Press 89-116.

[15]    Scholkopf B, Simard P, Smola A, Vapnik V. (2000). Prior knowledge in support vector kernels. Advances in Neural Information Processing Systems (12): 526-532.

[16]    Campbell C, Cristianini N, Smola AJ. (2000). Query learning with large margin classifiers. Proceedings of the 7th ICML, Stanford, pp. 111-118.

[17]    Lee YJ, Mangasarian OL. (2001). SSVM: A smooth support vector machine for classification. Computational Optimization and Applications 20(1): 5-22. https://doi.org/10.1023/A:1011215321374

[18]    Chen W, Wang L, Geng G, Mao W, Li X. (2012). Domain name credit evaluation method based on machine learning. Computer Application Research 29(2): 690-692.

[19]    Hopfield JJ. (1984). Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences of the United States of America 81(10): 3088. https://doi.org/10.1073/pnas.81.10.3088

[20]    Guo ZW, Xu LC, Zhu LQ, Cao YH. (1992). Theories and methods of macroscopic quality evaluation, enterprise development and system engineering. Beijing: China Science and Technology Press: 147-150.

[21]    Liao YL. (2010). Comparison of two CSI computing methods based on customer evaluation changes. Mathematical Statistics and Management 29(4): 743-753.

[22]    Zhu XD, Feng TJ. (2003). Personal credit evaluation based on GA neural network. System Engineering Theories and Practice 23(3): 48-51.

[23]    Huang YH., Zhu, JF. (2009). Research and application on projection pursuit cluster evaluation model based on accelerating genetic algorithm. System Engineering 27(11): 107-110.

[24]    Ni SY, Pan D, Wu CF. (2003). Comprehensive evaluation research on fund performance based on genetic algorithm. System Engineering 21(2): 1-6.