Attribute Selection Method based on Objective Data and Subjective Preferences in MCDM

  • Xiaofei Ma Technology Planning Dalian Commodity Exchange Dalian City, China, 116023
  • Yi Feng Technology Planning Dalian Commodity Exchange Dalian City, China, 116023
  • Yi Qu Technology Planning Dalian Commodity Exchange Dalian City, China, 116023
  • Yang Yu Agricultural Bank of China Data Center 88 Aoni Road, Pudong New Area Shanghai City, China, 200131


Decision attributes are important parameters when choosing an alternative in a multiple criteria decision-making (MCDM) problem. In order to select the optimal set of decision attributes, an analysis framework is proposed to illustrate the attribute selection problem. Then a two-step attribute selection procedure is presented based on the framework: In the first step, attributes are filtered by using correlation algorithm. In the second step, a multi-objective optimization model is constructed to screen attributes from the results of the first step. Finally, a case study is given to illustrate and verify this method. The advantage of this method is that both external attribute data and subjective decision preferences are utilized in a sequential procedure. It enhances the reliability of decision attributes and matches the actual decision-making scenarios better.


[1] Babaei, S., Sepehri, M. M., Babaei, E. (2015). Multi-objective portfolio optimization considering the dependence structure of asset returns. European Journal of Operational Research, 244(2), 525-539, 2015.

[2] Bermejo, P., Gamez, J. A., Puerta, J. M. (2014). Speeding up incremental wrapper feature subset selection with Naive Bayes classifier. Knowledge-Based Systems, 55, 140-147, 2014.

[3] Chen, Y., Kilgour, D. M., Hipel, K. W. (2008). Screening in multiple criteria decision analysis. Decision Support Systems, 45(2), 278-290, 2008.

[4] Chun, Y. H. (2015). Multi-attribute sequential decision problem with optimizing and satisficing attributes. European Journal of Operational Research, 243(1), 224-232, 2015.

[5] Comes, T., Hiete, M., Wijngaards, N., Schultmann, F. (2011). Decision maps: A framework for multi-criteria decision support under severe uncertainty. Decision Support Systems, 52(1), 108-118, 2011.

[6] Dai, J., Wang, W., Tian, H., Liu, L. (2013). Attribute selection based on a new conditional entropy for incomplete decision systems. Knowledge-Based Systems, 39, 207-213, 2013.

[7] Hapfelmeier, A., Ulm, K. (2014). Variable selection by Random Forests using data with missing values, Computational Statistics & Data Analysis, 80, 129-139, 2014.

[8] Huda, S., Abdollahian, M., Mammadov, M., Yearwood, J., Ahmed, S., Sultan, I. (2014): A hybrid wrapper-filter approach to detect the source (s) of out-of-control signals in multivariate manufacturing process, European Journal of Operational Research, 237(3), 857-870, 2014.

[9] Lin, Q., Li, J., Du, Z., Chen, J., Ming, Z. (2015). A novel multi-objective particle swarm optimization with multiple search strategies, European Journal of Operational Research, 247(3), 732-744, 2015.

[10] Ma XF, Zhong QY, Qu Y (2013) Determination method of emergency key property based on common knowledge model and Euclidean distance, Systems Engineering, 31(10), 93-97, 2013.

[11] Meinshausen, N., Buhlmann, P. (2010): Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4), 417-473, 2010.

[12] Meng MH, Pei XJ,Wu MQ (2015): Study on choice of factors influencing stability of perilous rock based on fuzzy multi-attribute group decision-making. Subgrade Engineering, 1:20-23, 2015.

[13] Montajabiha, M. (2016): An Extended PROMETHE II Multi-Criteria Group Decision Making Technique Based on Intuitionistic Fuzzy Logic for Sustainable Energy Planning. Group Decision and Negotiation, 25(2), 221-244, 2016.

[14] Robin, G., Jean-Michel P., Christine T. (2010): Variable selection using random forests, Pattern Recognition Letters, 31, 2225-2236, 2010.

[15] Shen HP, Zhang YP, Wang YK (2014): Research on regular Chinese fragments reassembly based on 0-1 programming model, Electronic Science and Technology, 6:13-16, 2014.

[16] Stewart, T. J. (1992): A critical survey on the status of multiple criteria decision making theory and practice, Omega, 20(5-6), 569-586, 1992.

[17] Wahlqvist, J., Van Hees, P. (2013): Validation of FDS for large-scale well-confined mechanically ventilated fire scenarios with emphasis on predicting ventilation system behavior, Fire Safety Journal, 62, 102-114, 2013.

[18] Wu, K. J., Tseng, M. L., Chiu, A. S., Lim, M. K. (2016): Achieving competitive advantage through supply chain agility under uncertainty: A novel multi-criteria decision-making structure, International Journal of Production Economics, article in press, 2016.

[19] Wu, K. J., Liao, C. J., Tseng, M. L., Lim, M. K., Hu, J., Tan, K. (2017): Toward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertainties, Journal of Cleaner Production, 142, 663-676, 2017.

[20] Zeleny, M., Cochrane, J. L. (1973): Multiple criteria decision making, University of South Carolina Press, 1973.

[21] Zhang, Y., Gong, D., Cheng, J. (2017): Multi-Objective Particle Swarm Optimization Approach for Cost-based Feature Selection in Classification, IEEe/ACM Transactions on Computational Biology and Bioinformatics, 14, 64-75, 2017.

[22] Zhu, J., Zhang, S., Chen, Y., Zhang, L. (2016): A hierarchical clustering approach based on three- dimensional gray relational analysis for clustering a large group of decision makers with double information, Group Decision and Negotiation, 25(2), 325-354, 2016.
How to Cite
MA, Xiaofei et al. Attribute Selection Method based on Objective Data and Subjective Preferences in MCDM. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 3, p. 391-407, may 2018. ISSN 1841-9844. Available at: <>. Date accessed: 22 jan. 2021. doi:


attribute selection, multi-criteria decision-making (MCDM), multiobjective optimization, attribute correlation